Detecting falls using a mobile device

ABSTRACT

In an example method, a mobile device obtains a signal indicating an acceleration measured by a sensor over a time period. The mobile device determines an impact experienced by the user based on the signal. The mobile device also determines, based on the signal, one or more first motion characteristics of the user during a time prior to the impact, and one or more second motion characteristics of the user during a time after the impact. The mobile device determines that the user has fallen based on the impact, the one or more first motion characteristics of the user, and the one or more second motion characteristics of the user, and in response, generates a notification indicating that the user has fallen.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 16/128,464, filed Sep. 11, 2018, which claimspriority to U.S. Provisional Application No. 62/565,988, filed Sep. 29,2017, the entire contents of each of which are incorporated herein byreference.

TECHNICAL FIELD

The disclosure relates to techniques for determining whether a user hasfallen using a mobile device.

BACKGROUND

A motion sensor is a device that measures the motion experienced by anobject (e.g., the velocity or acceleration of the object with respect totime, the orientation or change in orientation of the object withrespect to time, etc.). In some cases, a mobile device (e.g., a cellularphone, a smart phone, a tablet computer, a wearable electronic devicesuch as a smart watch, etc.) can include one or more motion sensors thatdetermine the motion experienced by the mobile device over a period oftime. If the mobile device is worn by a user, the measurements obtainedby the motion sensor can be used to determine the motion experienced bythe user over the period of time.

SUMMARY

Systems, methods, devices and non-transitory, computer-readable mediumsare disclosed for electronically determining whether a user has fallenusing a mobile device.

In an aspect, a method includes obtaining, by a mobile device, motiondata indicating motion measured by a motion sensor over a time period.The motion sensor is worn or carried by a user. The method also includesdetermining, by the mobile device, an impact experienced by the userbased on the motion data, the impact occurring during a first intervalof the time period. The method also includes determining, by the mobiledevice based on the motion data, one or more first motioncharacteristics of the user during a second interval of the time period.The second interval occurs prior to the first interval. The method alsoincludes determining, by the mobile device based on the motion data, oneor more second motion characteristics of the user during a thirdinterval of the time period. The third interval occurs after the firstinterval. The method also includes determining, by the mobile device,that the user has fallen based on the impact, the one or more firstmotion characteristics of the user, and the one or more second motioncharacteristics of the user, and responsive to determining that the userhas fallen, generating, by the mobile device, a notification indicatingthat the user has fallen.

Implementations of this aspect can include one or more of the followingfeatures.

In some implementations, determining the one or more first motioncharacteristics can include determining, based on the motion data, thatthe user was walking during the second interval.

In some implementations, determining the one or more first motioncharacteristics can include determining, based on the motion data, thatthe user was ascending or descending stairs during the second interval.

In some implementations, determining the one or more first motioncharacteristics can include determining, based on the motion data, thatthe user was moving a body part according to a flailing motion or abracing motion during the second interval.

In some implementations, determining the one or more second motioncharacteristics can include determining, based on the motion data, thatthe user was walking during the third interval.

In some implementations, determining the one or more second motioncharacteristics can include determining, based on the motion data, thatthe user was standing during the third interval.

In some implementations, determining the one or more second motioncharacteristics can include determining, based on the motion data, thatan orientation of a body part of the user changed N or more times duringthe third interval.

In some implementations, determining that the user has fallen caninclude determining, based on the motion data, that the impact isgreater than a first threshold value, and determining, based on themotion data, that a motion of the user was impaired during the thirdinterval.

In some implementations, determining that the user has fallen caninclude determining, based on the motion data, that the impact is lessthan a first threshold value and greater than a second threshold value,determining, based on the motion data, that the user was at least one ofwalking during the second interval, ascending stairs during the secondinterval, or descending stairs during the second interval, determining,based on the motion data, that the user was moving a body part accordingto a flailing motion or a bracing motion during the second interval, anddetermining, based on the motion data, that a motion of the user wasimpaired during the third interval.

In some implementations, generating the notification can includepresenting an indication that the user has fallen on at least one of adisplay device or an audio device of the mobile device.

In some implementations, generating the notification can includetransmitting data to a communications device remote from the mobiledevice, the data comprising an indication that the user has fallen.

In some implementations, determining that the user has fallen caninclude generating a statistical model based on one or more sampledimpacts, one or more sampled first motion characteristics, and one ormore sampled second motion characteristics. The one or more sampledimpacts, the one or more sampled first motion characteristics, and theone or more sampled second motion characteristics can be determinedbased on sample motion data. The sample motion data can indicate motionmeasured by one or more additional sensors over one or more additionaltime periods, where each additional motion sensor is worn by arespective additional user.

In some implementations, the statistical model can be a Bayesianstatistical model.

In some implementations, the one or more sampled first motioncharacteristics can include an indication of a type of activity beingperformed by a particular additional user with respect to the samplemotion data, an indication of an activity level of a particularadditional user with respect to the sample motion data, and/or anindication of a walking speed of a particular additional user withrespect to the sample motion data.

In some implementations, the method can be performed by a co-processorof the mobile device. The co-processor can be configured to receivemotion data obtained from one or more motion sensors, process the motiondata, and provide the processed motion data to one or more processors ofthe mobile device.

In some implementations, the mobile device can include the motionsensor.

In some implementations, the mobile device can be worn on an arm or awrist of the user while the motion is being measured by the sensor.

In some implementations, the mobile device can be a wearable mobiledevice.

In another aspect, a method includes obtaining, by a mobile device, afirst signal indicating an acceleration measured by an accelerometerover a time period, and a second signal indicating an orientationmeasured by an orientation sensor over the time period, wherein theaccelerometer and the orientation sensor are physically coupled to auser. The method also includes determining, by the mobile device,rotation data indicating an amount of rotation experienced by the userduring the time period, determining, by the mobile device, that the userhas tumbled based on the rotation data, and responsive to determiningthat the user has tumbled, generating, by the mobile device, anotification indicating that the user has tumbled.

Implementations of this aspect can include one or more of the followingfeatures.

In some implementations, the rotation data can include a third signalindicating a rotation rate experienced by the user during the timeperiod.

In some implementations, the rotation data can include an indication ofone or more rotational axes of the rotation in a reference coordinatesystem by the user during the time period.

In some implementations, the rotation data can include an indication ofan average rotational axis of the rotation by the user during the timeperiod.

In some implementations, determining that the user has tumbled caninclude determining a variation between the one or more rotational axesof the rotation by the user during the time period and the averagerotational axis of the rotation by the user during the time period.

In some implementations, determining that the user has tumbled caninclude determining that the variation is less than a first thresholdvalue, and responsive to determining that the variation is less than thefirst threshold value, determining a fourth signal corresponding to anangular displacement of the user during the time period based on thethird signal.

In some implementations, determining the fourth signal can includeintegrating the third signal with respect to the period of time.

In some implementations, determining that the user has tumbled caninclude determining that the angular displacement of the user during theperiod of time is greater than a second threshold value, determiningthat at least one of the one or more rotational axes of the rotation bythe user during the time period is greater than a third threshold value,and responsive to determining that the angular displacement experiencedof the user during the period of time is greater than the secondthreshold value and determining that at least one of the one or morerotational axes of the rotation by the user during the time period isgreater than the third threshold value, determining that the user hastumbled.

In some implementations, generating the notification can includepresenting an indication that the user has tumbled on at least one of adisplay device or an audio device of the mobile device.

In some implementations, generating the notification can includetransmitting data to a communications device remote from the mobiledevice, the data comprising an indication that the user has tumbled.

In some implementations, the method can be performed by a co-processorof the mobile device. The co-processor can be configured to receivemotion data obtained from one or more sensors, process the motion data,and provide the processed motion data to one or more processors of themobile device.

In another aspect, a method includes obtaining, by a mobile device,motion data indicating a motion measured by one or more motion sensorsover a first time period. The one or more motion sensors are worn by auser. The method also includes determining, by the mobile device, thatthe user has fallen based on the motion data, and responsive todetermining that the user has fallen, generating, by the mobile device,one or more notifications indicating that the user has fallen.

Implementations of this aspect can include one or more of the followingfeatures.

In some implementations, generating the one or more notifications caninclude presenting a first notification to the user indicating that theuser has fallen.

In some implementations, the first notification can include at least oneof a visual message, an audio message, or a haptic message.

In some implementations, generating the one or more notifications caninclude receiving, by the mobile device, an input from the user inresponse to the first notification. The input can indicate a request forassistance by the user. Further, generating the one or morenotifications can include, responsive to receiving the input,transmitting a second notification indicating the request for assistanceto a communications device remote from the mobile device.

In some implementations, the communications device can be an emergencyresponse system.

In some implementations, the second notification can indicate a locationof the mobile device.

In some implementations, generating the one or more notifications caninclude determining, by the mobile device, an absence of movement by theuser during a second time period after the user has fallen, andresponsive to determining the absence of movement by the user during thesecond time period, transmitting a second notification indicating arequest for assistance to a communications device remote from the mobiledevice.

In some implementations, generating the one or more notifications caninclude determining, by the mobile device, that the user has movedduring a second time period after the user has fallen, and responsive todetermining that the user has moved during the second time period,refraining from transmitting a second notification indicating a requestfor assistance to a communications device remote from the mobile device.

In some implementations, the one or more notifications can be generatedaccording to a state machine.

In some implementations, the one or more motion sensors can include atleast one of an accelerometer or a gyroscope.

In some implementations, the mobile device can be a wearable mobiledevice.

In some implementations, determining that the user has fallen caninclude determining that the user experienced an impact based on themotion data.

In some implementations, determining that the user has fallen caninclude determining a behavior of the user during the first time period.

In another aspect, a method includes obtaining, by a mobile device,sample data generated by a plurality of sensors over a time period. Theplurality of sensors is worn by a user. The sample data includes motiondata indicating a motion of the user obtained from one or more motionsensors of the plurality of sensors, and at least one of location dataindicating a location of the mobile device obtained from one or morelocation sensors of the plurality of sensors, altitude data indicatingan altitude of the mobile device obtained from one or more altitudesensors of the plurality of sensors, or heart rate data indicating aheart rate of the user obtained from one or more heart rate sensor ofthe plurality of sensors. The method also includes determining, by themobile device, that the user has fallen based on the sample data, andresponsive to determining that the user has fallen, generating, by themobile device, one or more notifications indicating that the user hasfallen.

Implementations of this aspect can include one or more of the followingfeatures.

In some implementations, the one or more motion sensors can include atleast one of an accelerometer or a gyroscope.

In some implementations, obtaining the motion data can include obtainingacceleration data using the accelerometer during a first time intervalduring the period of time. The gyroscope can be disabled during thefirst time interval. Obtaining the motion data can further includedetermining, based on the accelerometer data obtained during the firsttime interval, that a movement of a user exceeded a threshold levelduring the first time interval, and responsive to determining that themovement of the user exceeded the threshold level during the first timeinterval, obtaining acceleration data using the accelerometer andgyroscope data using the gyroscope during a second time interval afterthe first time interval.

In some implementations, the one or more altitude sensors can include atleast one of an altimeter or a barometer.

In some implementations, the one or more location sensors can include atleast one of a wireless transceiver or a global Navigation SatelliteSystem receiver.

In some implementations, determining that the user has fallen caninclude determining, based on the motion data, a change in orientationof the mobile device during the period of time, and determining that theuser has fallen based on the change in orientation.

In some implementations, determining that the user has fallen caninclude determining, based on the motion data, an impact experienced bythe user during the period of time, and determining that the user hasfallen based on the impact.

In some implementations, determining that the user has fallen caninclude determining, based on the altitude data, a change in altitude ofthe mobile device during the period of time, and determining that theuser has fallen based on the change in altitude.

In some implementations, determining that the user has fallen caninclude determining, based on the heart rate data, a change in heartrate of the user during the period of time, and determining that theuser has fallen based on the change in heart rate.

In some implementations, determining the change in heart rate of theuser during the period of time can include determining a rate of decayof the heart rate of the user during the period of time.

In some implementations, determining that the user has fallen caninclude determining, based on the location data, an environmentalcondition at the location of the mobile device, and determining that theuser has fallen based on the environment conditional.

In some implementations, the environmental condition can be weather.

In some implementations, the mobile device can determine that the userhas fallen based on the motion data, the location data, the altitudedata, and the heart rate data.

In some implementations, the mobile device can be a wearable mobiledevice.

In some implementations, generating the one or more notifications caninclude transmitting a notification to a communications device remotefrom the mobile device.

In some implementations, the communications device can be an emergencyresponse system.

Particular implementations provide at least the following advantages. Insome cases, the implementations described herein can be used todetermine whether a user has fallen, and in response, automatically takean appropriate action. As an example, a mobile device can monitor themovement of a user as he goes about his daily life (e.g., walking,running, sitting, laying down, participating in a sport or athleticactivity, and so forth). Based on the user's movements, the mobiledevice can determine whether a user has fallen, and if so, whether theuser may be in need of assistance (e.g., physical assistance in standingand/or recovering from the fall, medical attention to treat injuriessustained in the fall, and so forth). In response, the mobile device canautomatically notify others (e.g., a caretaker, a physician, medicalresponder, or a bystander) of the situation, such that they can provideassistance to the user. Thus, assistance can be rendered to the usermore quickly and effectively.

Further, the implementations described herein can be used to determinewhether a user has fallen and/or whether the user may be in need ofassistance more accurately. Thus, resources can be more effectivelyused. For instance, the mobile device can determine whether the user hasfallen and/or whether the user may be in need of assistance with fewerfalse positives. Thus, the mobile device is less likely to consumecomputational and/or network resources to generate and transmitnotifications to others when the user does not need assistance. Further,medical and logistical resources can be deployed to assist a user with agreater degree of confidence that they are needed, thereby reducing thelikelihood of waste. Accordingly, resources can be consumed moreefficiently, and in a manner that increases the effective responsecapacity of a system.

Other implementations are directed to systems, devices andnon-transitory, computer-readable mediums.

The details of one or more embodiments are set forth in the accompanyingdrawings and the description below. Other features and advantages willbe apparent from the description and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram of an example system for determining whether a userhas fallen and/or may be in need of assistance.

FIG. 2A is a diagram showing an example position of a mobile device on auser's body.

FIG. 2B is a diagram showing example directional axes with respect amobile device.

FIG. 3 is a diagram showing an example acceleration signal obtained by amobile device.

FIG. 4 is a diagram of an example heat map of motion data collected froma sample population of users that did not fall while performing physicalactivities, and data points indicating motion data collected from asample population of users that fell.

FIG. 5 is a diagram of another example heat map of motion data collectedfrom a sample population of users that did not fall while performingphysical activities, and data points indicating motion data collectedfrom a sample population of users that fell.

FIG. 6 is a diagram of another example heat map of motion data collectedfrom a sample population of users that did not fall while performingphysical activities, and data points indicating motion data collectedfrom a sample population of users that fell.

FIG. 7 is a diagram of an example technique for determining a durationof time during which the magnitude of an acceleration vector was below athreshold value.

FIG. 8 is a diagram of an example decision tree for determining whethera user is exhibiting signs of trauma and/or impairment in motion.

FIG. 9 is a diagram of an example decision tree for determining thateither a user has fallen and may be in need of assistance, or a user haseither not fallen or has fallen but is not in need of assistance.

FIG. 10 is a diagram of an example scatter plot of pose angle datacollected from a sample population of users that had fallen.

FIG. 11A shows a plot of example acceleration signals obtained by amobile device over a sliding sample window.

FIG. 11B shows a plot of example rate of rotation signals with respectto the inertia frame obtained by a mobile device over a sliding samplewindow.

FIG. 11C shows an axis of rotation signal indicating the instantaneousaxis of rotation of a mobile device over the sliding sample window.

FIG. 11D shows a total angular displacement signal corresponding to thetotal angular displacement of a mobile device over a sliding samplewindow.

FIG. 12 is a diagram of an example state machine for determining whetherto transmit a distress call using a mobile device.

FIG. 13 is a schematic representation of a fall detection techniquebased on user-specific sensitivity.

FIG. 14 is a diagram of an example state machine for selectivelyenabling and disabling a gyroscope.

FIG. 15 is a schematic representation of a fall detection techniquebased on multiple types of sensor measurements.

FIG. 16 is a schematic representation of an example usage of anaccelerometer and gyroscope in conjunction to determine informationregarding the motion of a user

FIG. 17 is a schematic representation of an example fall classifier.

FIG. 18 is a schematic representation of an example fall sensor fusionmodule.

FIG. 19 is a schematic representation of an example distress callmodule.

FIG. 20 is a schematic representation of an example fall state machine.

FIG. 21 is a flow chart diagram of an example process for determiningwhether a user has fallen and/or may be in need of assistance.

FIG. 22 is a flow chart diagram of an example process for determiningwhether a user has tumbled and/or may be in need of assistance.

FIG. 23 is a flow chart diagram of an example process for determiningwhether a user has fallen and/or may be in need of assistance.

FIG. 24 is a flow chart diagram of an example process for determiningwhether a user has fallen and/or may be in need of assistance.

FIG. 25 is a block diagram of an example architecture for implementingthe features and processes described in reference to FIGS. 1-24.

DETAILED DESCRIPTION Overview

FIG. 1 shows an example system 100 for determining whether a user hasfallen and/or may be in need of assistance. The system 100 includes amobile device 102, a server computer system 104, communications devices106, and a network 108.

In an example usage of the system 100, a user 110 positions the mobiledevice 102 on his body, and goes about his daily life. This can include,for example, walking, running, sitting, laying down, participating in asport or athletic activity, or any other physical activity. During thistime, the mobile device 102 collects sensor data regarding movement ofthe mobile device 102, an orientation of the mobile device 102, and/orother dynamic properties. Based on this information, the system 100determines whether the user has fallen, and if so, whether the user maybe in need of assistance.

As an example, the user 110 may stumble while walking and fall to theground. Further, after falling, the user 110 may be unable to standagain on his own and/or have suffered from an injury as a result of thefall. Thus, he may be in need of assistance, such as physical assistancein standing and/or recovering from the fall, medical attention to treatinjuries sustained in the fall, or other help. In response, the system100 can automatically notify others of the situation. For example, themobile device 102 can generate and transmit a notification to one ormore of the communications devices 106 to notify one or more users 112(e.g., caretakers, physicians, medical responders, emergency contactpersons, etc.) of the situation, such that they can take action. Asanother example, the mobile device 102 can generate and transmit anotification to one or more bystanders in proximity to the user (e.g.,by broadcasting a visual and/or auditory alert), such they can takeaction. As another example, the mobile device 102 can generate andtransmit a notification to the server computer system 104 (e.g., torelay the notification to others and/or to store the information forfuture analysis). Thus, assistance can be rendered to the user 110 morequickly and effectively.

In some cases, the system 100 can determine that the user 110 hasexperienced an external force, but has not fallen and is not in need ofassistance. As an example, the user 110 may have been contacted duringan athletic activity (e.g., bumped by another while playing basketball),but has not fallen due to the contact and is able to recover withoutassistance from others. Accordingly, the system 100 can refrain fromgenerating and transmitting a notification to others.

In some cases, the system 100 can determine that the user 110 hasfallen, but that the user is not in need of assistance. As an example,the user 110 may have fallen as a part of an athletic activity (e.g.,slipped while skiing), but is able to recover without assistance fromothers. Accordingly, the system 100 can refrain from generating andtransmitting a notification to others.

In some cases, the system 100 can make these determinations based onmotion data obtained before, during, and/or after an impact experiencedby the user 110. For example, the mobile device 102 can collect motiondata (e.g., an acceleration signal obtained by a motion sensor of themobile device 102), and the system 100 can use the motion data toidentify a point in time at which the user experienced an impact. Uponidentifying the impact time, the system 100 can analyze the motion dataobtained during the impact, prior to the impact, and/or after the impactto determine whether the user has fallen, and if so, whether the usermay be in need of assistance.

The implementations described herein enable the system 100 to determinewhether a user has fallen and/or whether the user may be in need ofassistance more accurately, such that resources can be more effectivelyused. For instance, the system 100 can determine whether the user hasfallen and/or whether the user may be in need of assistance with fewerfalse positives. Thus, the system 100 is less likely to consumecomputational and/or network resources to generate and transmitnotifications to others when the user does not need assistance. Further,medical and logistical resources can be deployed to assist a user with agreater degree of confidence that they are needed, thereby reducing thelikelihood of waste. Accordingly, resources can be consumed moreefficiently, and in a manner that increases the effective responsecapacity of one or more systems (e.g., a computer system, acommunications system, and/or an emergency response system).

The mobile device 102 can be any portable electronic device forreceiving, processing, and/or transmitting data, including but notlimited to cellular phones, smart phones, tablet computers, wearablecomputers (e.g., smart watches), and the like. The mobile device 102 iscommunicatively connected to server computer system 104 and/or thecommunications devices 106 using the network 108.

The server computer system 104 is communicatively connected to mobiledevice 102 and/or the communications devices 106 using the network 108.The server computer system 104 is illustrated as a respective singlecomponent. However, in practice, it can be implemented on one or morecomputing devices (e.g., each computing device including at least oneprocessor such as a microprocessor or microcontroller). A servercomputer system 104 can be, for instance, a single computing device thatis connected to the network 108. In some implementations, the servercomputer system 104 can include multiple computing devices that areconnected to the network 108. In some implementations, the servercomputer system 104 need not be located locally to the rest of thesystem 100, and portions of a server computer system 104 can be locatedin one or more remote physical locations.

A communications device 106 can be any device that is used to transmitand/or receive information transmitted across the network 108. Examplesof the communications devices 106 include computers (such as desktopcomputers, notebook computers, server systems, etc.), mobile devices(such as cellular phones, smartphones, tablets, personal dataassistants, notebook computers with networking capability), telephones,faxes, and other devices capable of transmitting and receiving data fromthe network 108. The communications devices 106 can include devices thatoperate using one or more operating system (e.g., Microsoft Windows,Apple OS X, Linux, Unix, Android, Apple iOS, etc.) and/or architectures(e.g., x86, PowerPC, ARM, etc.) In some implementations, one or more ofthe communications devices 106 need not be located locally with respectto the rest of the system 100, and one or more of the communicationsdevices 106 can be located in one or more remote physical locations.

The network 108 can be any communications network through which data canbe transferred and shared. For example, the network 108 can be a localarea network (LAN) or a wide-area network (WAN), such as the Internet.As another example, the network 108 can be a telephone or cellularcommunications network. The network 108 can be implemented using variousnetworking interfaces, for instance wireless networking interfaces (suchas Wi-Fi, Bluetooth, or infrared) or wired networking interfaces (suchas Ethernet or serial connection). The network 108 also can includecombinations of more than one network, and can be implemented using oneor more networking interfaces.

As described above, a user 110 can position the mobile device 102 on hisbody, and go about his daily life. As an example, as shown in FIG. 2A,the mobile device 102 can be a wearable electronic device or wearablecomputer (e.g., a smart watch), that is secured to a wrist 202 of theuser 110. The mobile device 102 can be secured to the user 110, forexample, through a band or strap 204 that encircles the wrist 202.Further, the orientation of the mobile device 102 can differ, depend onthe location at which is it placed on the user's body and the user'spositioning of his body. As an example, the orientation 206 of themobile device 102 is shown in FIG. 2A. The orientation 206 can refer,for example, to a vector projecting from a front edge of the mobiledevice 102 (e.g., the y-axis shown in FIG. 2B).

Although an example mobile device 102 and an example position of themobile device 102 is shown, it is understood that these are merelyillustrative examples. In practice, the mobile device 102 can be anyportable electronic device for receiving, processing, and/ortransmitting data, including but not limited to cellular phones, smartphones, tablet computers, wearable computers (e.g., smart watches), andthe like. As an example, the mobile device 102 can be implementedaccording to the architecture 2500 shown and described with respect toFIG. 25. Further, in practice, the mobile device 102 can be positionedon other locations of a user's body (e.g., arm, shoulder, leg, hip,head, abdomen, hand, foot, or any other location).

As the user 110 goes about his daily life with the mobile device 102 onhis body (e.g., walks, runs, sits, lays down, participates in a sport orathletic activity, or any other physical activity), the mobile device102 collects sensor data regarding the motion of the user 110. Forinstance, using the motion sensors 2510 shown in FIG. 25 (e.g., one ormore accelerometers), the mobile device 102 can measure an accelerationexperienced by the motion sensors 2510, and correspondingly, theacceleration experienced by the mobile device 102. Further, using themotion sensors 2510 (e.g., one or more compasses or gyroscopes), themobile device 102 can measure an orientation of the motion sensors 2510,and correspondingly, an orientation of the mobile device 102. In somecases, the motion sensors 2510 can collect data continuously orperiodically over a period of time or in response to a trigger event. Insome cases, the motion sensors 2510 can collect motion data with respectto one or more specific directions relative to the orientation of themobile device 102. For example, the motion sensors 2510 can collectsensor data regarding an acceleration of the mobile device 102 withrespect to the x-axis (e.g., a vector projecting from a side edge of themobile device 102, as shown in FIG. 2B), the y-axis (e.g., a vectorprojecting from a front edge of the mobile device 102, as shown in FIG.2B) and/or the z-axis (e.g., a vector projecting from a top surface orscreen of the mobile device 102, as shown in FIG. 2B), where the x-axis,y-axis, and z-axis refer to a Cartesian coordinate system in a frame ofreference fixed to the mobile device 102 (e.g., a “body” frame).

As an example, as shown in FIG. 3, as the user 110 moves, the mobiledevice 102 can use the motion sensors 2510 to continuously orperiodically collect sensor data regarding an acceleration experiencedby the motion sensors 2510 with respect to y-axis over a period of time.The resulting sensor data can be presented in the form of a time-varyingacceleration signal 300. In some cases, the acceleration system 300 canobtain acceleration samples at a sample frequency of 800 Hz using themotion sensors 2510, with a sampling bandwidth of 200 Hz. In practice,other sampling frequencies and/or sampling bandwidths are also possible.

In the example above, the acceleration signal 300 indicates theacceleration experienced by the mobile device 102 with respect to they-axis of the mobile device. In some cases, the acceleration signal 300can also indicate the acceleration experienced by the mobile device 102with respect to multiple different directions. For example, theacceleration signal 300 can include an x-component, a y-component, and az-component, referring to the acceleration experienced by the mobiledevice 102 with respect to the x-axis, the y-axis, and the z-axis of themobile device 102, respectively. Each component also can be referred asa channel of the acceleration signal (e.g., “x-channel,” the“y-channel,” and the “z-channel”).

The mobile device 102 can analyze the acceleration signal 300 todetermine whether the user has fallen. For instance, if the user hasfallen, the mobile device 102 may experience a relatively strong impact(e.g., when the user's body strikes the ground). Such an impact can beidentified based on a magnitude of the acceleration experienced by themobile device 102 (e.g., the rate of change in the velocity of themobile device), a magnitude of the jerk experienced by the mobile device(e.g., the rate of change in the acceleration of the mobile device), andan oscillatory behavior of the acceleration experienced by the mobiledevice 102. Each of these parameters can be determined using theacceleration signal 300.

As an example, the magnitude of the acceleration experienced by themobile device 102 can be determined, for each channel of theacceleration signal, using the relationship:

mag=max(abs(a(n))),

where mag is the magnitude of acceleration for that channel, a(n) is thenth sample of the acceleration signal 300 for that channel, and max isthe maximum calculated over a sliding window of samples of theacceleration signal 300, n_(window). In some cases, n_(window) cancorrespond to the number of samples spanning an interval of time of 0.2seconds or approximately 0.2 second. For example, if the samplingfrequency for the acceleration signal 300 is 800 Hz, n_(window) can be160. In practice, other values for n_(window) are also possible.

Alternatively, the magnitude of the acceleration experienced by themobile device 102 can be determined, for each channel of theacceleration signal, using the relationship:

mag=max(a(n))−min(a(n)),

where mag is the magnitude of acceleration for that channel, a(n) is thenth sample of the acceleration signal 300 for that channel, max is themaximum calculated over a sliding window of samples n_(window), and minis the minimum calculated over the window of samples the accelerationsignal 300, n_(window). As above, in some cases, n_(window) cancorrespond to the number of samples spanning an interval of time of 0.2seconds or approximately 0.2 second, though in practice, other valuesfor n_(window) are also possible.

If the acceleration signal 300 includes acceleration measurements withrespect to a single direction (e.g., having a single channel, such as ay-channel), the magnitude of the acceleration with respect to thatdirection can be determined using the relationship above. The resultingvalue is representative of the magnitude of the acceleration for theacceleration signal 300. Alternatively, the total energy from all threechannels over the window of interest (e.g. n_(window)) may be used asthe total magnitude of acceleration. For example, one notion of totalenergy could be computed as:

mag=sqrt(max(|x|)²+max(|y|)²+max(|z|)²).

If the acceleration signal 300 includes acceleration measurements withrespect to multiple directions (e.g., having multiple channels, such asa x-channel, a y-channel, and a z-channel), the magnitude of theacceleration with respect to each direction can be individuallydetermined using the relationship above, resulting in three individualmagnitude values (corresponding to the three channels, respectively).The greatest magnitude value can be selected as representative of themagnitude of the acceleration for the acceleration signal 300.

As another example, the magnitude of the jerk experienced by the mobiledevice 102 can be determined, for each channel of the acceleration,using the relationship:

${{jerk} = \frac{{abs}\left( {{a(n)} - {a\left( {n - 1} \right)}} \right)}{\Delta T}},$

where jerk is the magnitude of the jerk for that channel, a(n) is thenth sample of the acceleration signal 300 for that channel, and ΔT issampling interval of the acceleration signal 300 (e.g., the inverse ofthe sampling frequency).

If the acceleration signal 300 includes acceleration measurements withrespect to a single direction (e.g., having a single channel, such as ay-channel), the magnitude of the jerk with respect to that direction canbe determined using the relationship above. The resulting value isrepresentative of the magnitude of the jerk for the acceleration signal300.

If the acceleration signal 300 includes acceleration measurements withrespect to multiple directions (e.g., having multiple channels, such asa x-channel, a y-channel, and a z-channel), the magnitude of the jerkwith respect to each direction can be individually determined using therelationship above, resulting in three individual magnitude values(corresponding to the three channels, respectively). The greatestmagnitude value can be selected as representative of the magnitude ofthe jerk for the acceleration signal 300.

As another example, the oscillatory behavior of the accelerationexperienced by the mobile device 102 can be determined by identifying a“third-zero crossing” of the acceleration signal 300. The third-zerocrossing refers to the point in time at which the acceleration signal300 changes from a negative value to a positive value, or vice versa,for the third time for a particular period of oscillation (e.g.,“crosses” zero for the third time). As an example, within the windowshown in FIG. 3, the third-zero crossing of the acceleration signal 300occurs at point 302. The time between the maximum value of theacceleration signal 300 (e.g., point 304) and the third-zero crossingpoint 302 after that maximum value is referred to as the time to thethird-zero crossing 306. This value can be used as an estimate for theperiod of oscillation of the acceleration signal 300, and can be used toestimate the frequency of oscillation of the acceleration signal 300.This can be useful, for example, as the impact response of thecomponents of the motion sensor (e.g., microelectromechanical systems[MEMS] components) can be similar to that of a second-order underdampedsystem. Thus, the period or frequency of oscillation can be used as anapproximation of the response.

The magnitude of the acceleration experienced by the mobile device 102,the magnitude of the jerk experienced by the mobile device 102, and theoscillatory behavior of the acceleration experienced by the mobiledevice 102 (e.g., the third-zero crossing of the acceleration signal300) can be used in conjunction to identify impacts that may beindicative of a user falling. In some cases, this determination can bemade using a statistical or probabilistic model (e.g., a Bayesianmodel).

For example, FIG. 4 shows a heat map 400 of motion data collected from asample population of users as they performed daily activities (e.g.,“active daily life”). This motion data does not include any motion dataresulting from a user falling. The heat map 400 compares (i) themagnitude of the acceleration experienced by a mobile device (x-axis),and (ii) the magnitude of jerk experienced by a mobile device (y-axis)for the sample population. Further, the distribution of motion data inthe heat map 400 with respect to each axis is shown by a respectivecumulative data function 402 a or 402 b. As shown in FIG. 4, the motiondata is primarily concentrated in a region corresponding to a relativelylow magnitude of acceleration, combined with a relatively low magnitudeof jerk.

FIG. 4 also shows several points 404 a and 404 b representing motiondata collected from a sample population of users that fell. Circularpoints 404 a indicate “radial dominant falls,” and square points 404 bindicate “tangential dominant falls.” These terms refer to the primarymovement direction of the mobile device 102 as the user fell (e.g., themotion of a user's arm and wrist as he falls) with respect to theoscillatory movement of the mobile device 102 as a user walks (e.g., theswinging motion of the user's arm and wrist as he walks). As shown inFIG. 4, each of these points 404 a and 404 b are situated in a regioncorresponding to a relatively higher magnitude of acceleration, combinedwith a relatively higher magnitude of jerk. Accordingly, impacts thatmay be indicative of a user falling can be identified, at least in part,by identifying motion data having a sufficiently high magnitude ofacceleration (e.g., greater than a particular threshold accelerationvalue), combined with a sufficiently high magnitude of jerk (e.g.,greater than a particular threshold jerk value). In practice, thethreshold acceleration value and/or the threshold jerk value can vary(e.g., based on empirically collected sample data).

As another example, FIG. 5 shows a heat map 500 of motion data collectedfrom a sample population of users as they performed daily activities(e.g., “active daily life”). As with the heat map 400 of FIG. 4, thismotion data also does not include any motion data resulting from a userfalling. The heat map 500 compares (i) the magnitude of the accelerationexperienced by a mobile device (x-axis), and (ii) the time to thethird-zero crossing of an acceleration signal obtained by a mobiledevice (y-axis) for the sample population. Further, the distribution ofmotion data in the heat map 500 with respect to each axis is shown by arespective cumulative data function 502 a or 502 b. As shown in FIG. 5,the motion data is primarily concentrated in a region corresponding to arelatively low magnitude of acceleration, combined with a relativelyhigh time to the third-zero crossing of an acceleration signal.

FIG. 5 also shows several points 504 a and 504 b representing motiondata collected from a sample population of users that fell. In a similarmanner as with FIG. 4, circular points 504 a indicate “radial dominantfalls,” and square points 504 b indicate “tangential dominant falls.” Asshown in FIG. 5, each of these points 504 a and 504 b are situated in aregion corresponding to a relatively higher magnitude of acceleration,combined with a relatively lower time to the third-zero crossing of anacceleration signal. Accordingly, impacts that may be indicative of auser falling can be identified, at least in part, by identifying motiondata having a sufficiently high magnitude of acceleration (e.g., greaterthan a particular threshold acceleration value), combined with asufficiently low time to the third-zero crossing of an accelerationsignal (e.g., less than a particular threshold time value). In practice,the threshold acceleration value and/or the threshold time value canvary (e.g., based on empirically collected sample data).

As described herein, the mobile device 102 can collect motion data, anduse the motion data to identify a point in time at which the userexperienced an impact. Upon identifying the impact time, the mobiledevice 102 can analyze motion data obtained during the impact, prior tothe impact, and/or after the impact to determine whether the user hasfallen, and if so, whether the user may be in need of assistance. Insome cases, the mobile device 102 can facilitate this analysis bycontinuously collecting and buffering motion data. Motion data caninclude an acceleration signal describing an acceleration experienced bythe mobile device 102 (e.g., an acceleration signal 300) and/or anorientation signal describing an orientation of the mobile device 102(e.g., a signal representing orientation measurements obtained using themotion sensors 2510, such as using a gyroscope or a compass). As anexample, the mobile device 102 can retain, on a running basis, portionsof an acceleration signal and portions of an orientation signal, eachcorresponding to a sliding window of time t. Upon identifying an impact(e.g., using the acceleration signal, as described above), the mobiledevice 102 can retrieve portions of the buffered motion datacorresponding to measurements obtained during the impact, prior to theimpact, and/or after the impact, and analyze those portions to make adetermination regarding the user's condition.

For instance, the mobile device 102 can analyze portions of anacceleration signal and portions of an orientation signal correspondingto measurements obtained prior to an identified impact (“pre-impact”information). Based on the analysis, the mobile device 102 can determinewhether the impact likely corresponds to the user falling. In somecases, this determination can be made using a statistical orprobabilistic model.

As an example, certain types of activities by a user may be more likelyto trigger a fall (e.g., walking, running, navigating stairs, or othersuch activities). Accordingly, an activity classifier can be used toidentify whether a user was performing one of these types of activitiesprior to the impact. If so, the likelihood that the user has fallen canbe higher. Otherwise, the likelihood that the user has fallen can belower.

An activity classifier can be implemented, for example, by collectingmotion data from a sample population (e.g., acceleration signals and/ororientation signals describing motion by each individual of the samplepopulation). Further, information can be collected regarding thephysical activities being performed by the sample population (e.g., anindication of the physical activity being performed by each individualat the time that the motion data was measured, such as walking, running,biking, golfing, boxing, skiing, playing basketball, or any otheractivity). This information can be obtained, for example, from theserver computer system 104 (e.g., a server computer system to collectand aggregate data from multiple different mobile devices). Based onthis information, particular traits or patterns can be determined foreach different type of physical activity. Accordingly, when motion datais collected from an additional user, the motion data can be comparedagainst the previously collected motion data to classify that user'sactivity.

Further, an activity classifier can be implemented using varioustechniques. For example, in some cases, an activity classifier can beimplemented through the use of “machine learning” techniques such asdecision tree learning, association rule learning, artificial neuralnetworks, deep learning, inductive logic programming, support vectormachines clustering, Bayesian networks, reinforcement learning,representation learning, similarity and metric learning, sparsedictionary learning, genetic algorithms, rule-based machine learning,learning classifier systems, among others.

As another example, the activity level of a user can indicate whether auser is more likely to have fallen and is in need of assistance. Forinstance, an athletic user who is highly active (e.g., historicallyexhibits frequent and intense movements) may be at a lower risk offalling in a manner that requires assistance. However, a frail user whois not active (e.g., historically exhibits infrequent and slightmovements) may be at a higher risk of falling in a manner that requiresassistance. Accordingly, an activity level classifier can be used toidentify the activity level of a user, and the likelihood that the userhas fallen and is in need of assistance can be adjusted based on theclassification.

An activity level classifier can also be implemented, for example, bycollecting motion data from a sample population (e.g., accelerationsignals and/or orientation signals describing motion by each individualof the sample population). Further, information can be collectedregarding the activity levels of the sample population (e.g., anindication of how athletic, physically healthy, and/or ambulatory eachindividual was at the time that the motion data was measured). Thisinformation can be obtained, for example, from the server computersystem 104 (e.g., a server computer system to collect and aggregate datafrom multiple different mobile devices). Based on this information,particular traits or patterns can be determined for each different typeof activity level. Accordingly, when motion data is collected from anadditional user, the motion data can be compared against the previouslycollected motion data to classify that user's activity level.

Further, an activity level classifier also can be implemented usingvarious techniques. For example, in some cases, an activity levelclassifier can be implemented through the use of “machine learning”techniques such as decision tree learning, association rule learning,artificial neural networks, deep learning, inductive logic programming,support vector machines clustering, Bayesian networks, reinforcementlearning, representation learning, similarity and metric learning,sparse dictionary learning, genetic algorithms, rule-based machinelearning, learning classifier systems, among others.

In some cases, the statistical or probabilistic model can consideradditional information regarding the user, such as the user's age, thewalking speed of the user, the number of steps that a user takes perday, the number of calories expended by the user per day, a maximumexertion of the user during a day, or other characteristics of the user.The likelihood that a user has fallen can be increased or decreasedbased on each of these characteristics. For instance, a user who isolder may be at a higher risk of falling in a manner that requiresassistance, while a user who is younger may be at a lower risk offalling in a manner that requires assistance. Further, a user who has alower maximum walking speed may be at a higher risk of falling in amanner that requires assistance, while a user who has a higher maximumwalking speed may be at a lower risk of falling in a manner thatrequires assistance. Further, multiple different characteristics can beused in conjunction to determine the falling risk of a user. As anexample, the statistical or probabilistic model can include amulti-dimensional risk assessment model (e.g., a multi-dimensional riskmatrix), with each dimension corresponding to a different characteristicof the user and its contribution to the overall risk of the user.Information regarding each user can be collected, from the servercomputer system 104 (e.g., a server computer system to collect andaggregate data from multiple different mobile devices) and/or userinputs.

In some cases, the mobile device 102 can consider other types ofmovement made by the user prior to an identified impact. As anotherexample, when a user is falling, a user will often attempt to bracehimself from the ground. Accordingly, a mobile device 102 can determinewhether a user was performing a bracing motion with his arms prior to anidentified impact. If so, the likelihood that the user has fallen can behigher. Otherwise, the likelihood that the user has fallen can be lower.

When performing a bracing motion, a user's hand, and correspondingly themobile device 102, may accelerate towards the ground for a period oftime. Thus, the mobile device 102 may observe a positive acceleration inthe inertial z-direction (e.g., the direction perpendicular to ground,or the direction of gravity). By measuring the acceleration in theinternal z-direction, the mobile device 102 can determine a duration ofthe fall. For example, the duration of the fall can be estimated as theperiod of time in which the acceleration signal along the inertialz-direction is above a particular threshold value.

As another example when the user is falling, he may often flail hisarms. For instance, if a user slips, trips, or tumbles while walking,the user may move his arms erratically or chaotically prior to impactingthe ground in an attempt to balance himself. Thus, a mobile device 102can determine whether a user was flailing his arms prior to anidentified impact. If so, the likelihood that the user has fallen can behigher. Otherwise, the likelihood that the user has fallen can be lower.

A flailing motion can be detected, in part, by estimating the mobiledevice's “pose angle,” or orientation with respect to the inertial frame(e.g., the earth frame). This can be determined, for example, using boththe acceleration signal (e.g., to identify the direction of gravity, orthe inertial z-direction) and the orientation signal (e.g., to determinethe orientation of the device with respect to the direction of gravity).Using this information, a change in the pose angle of the mobile device102 over time also can be determined (e.g., the maximum difference inpose angle over a sample window, such as n_(window)). A relativelylarger change in pose angle over a period of time (e.g., a large changein orientation of the mobile device 102, and correspondingly, theorientation of the user's wrist and arm) can indicate that the user ismore likely to be performing a flailing motion.

In some cases, the amount of “chaos” in a user's motions can bedetermined by obtaining an acceleration signal corresponding to themotions of a user during a period of time, and determining the pathlength of the acceleration signal. When a user moves erratically (e.g.,performs a flailing motion), the acceleration signal will exhibit ahigher degree of variation during that period of time. Accordingly, thepath length of the acceleration signal will be longer. In contrast, whena user moves less erratically, the acceleration signal will exhibit alesser degree of variation during that period of time. Accordingly, thepath length of the acceleration signal will be shorter.

In some cases, the path length of the acceleration signal can becalculated using the equation:

(path length of acceleration signal)=Σ|a_(n)−a_(n−1)|

where a_(n) is the nth sample in an acceleration signal. The path lengthcan be determined by performing a summation of samples obtained over asliding window (e.g., a 1.4 second window around a fall or impact). Insome cases, chaos can be determined using other techniques (e.g., bymeasuring the entropy of the acceleration signal).

This “pre-impact” information can be used in conjunction to determinewhether the user is likely to have fallen. For example, FIG. 6 shows aheat map 600 of motion data collected from a sample population of usersas they performed daily activities (e.g., “active daily life”). Thismotion data does not include any motion data resulting from a userfalling. The heat map 600 compares (i) the change in pose angle of amobile device (x-axis), and (ii) the fall duration of the mobile device(y-axis) for the sample population. Further, the distribution of motiondata in the heat map 600 with respect to each axis is shown by arespective cumulative data function 602 a or 602 b. As shown in FIG. 6,the motion data is primarily concentrated in a region corresponding to arelatively low change in pose angle, combined with a relatively low fallduration.

FIG. 6 also shows several points 604 representing motion data collectedfrom a sample population of users that fell, where the motion data wascollected immediately prior to the users falling. As shown in FIG. 6,each of these points 604 are situated in a region corresponding to arelatively higher change in pose angle, combined with a relativelyhigher fall duration. Accordingly, a user falling can be identified, atleast in part, by identifying motion data—collected immediately prior toan identified impact—having a sufficiently large change in pose angle(e.g., greater than a particular threshold angle), combined with asufficiently long fall duration (e.g., greater than a particularthreshold amount of time). In practice, the threshold angle and/or thethreshold amount of time can vary (e.g., based on empirically collectedsample data).

The mobile device 102 can also analyze portions of an accelerationsignal and portions of an orientation signal corresponding tomeasurements obtained after an identified impact (“post-impact”information). Based on the analysis, the mobile device 102 can determinewhether the user may be in need of assistance. In some cases, thisdetermination can be made using a statistical or probabilistic model.

As an example, if a user falls and is injured or in distress due thefall, the user may exhibit signs of trauma and/or impairment in motion.Signs of trauma and/or impairment in motion can be identified based onthe acceleration signal and/or the orientation signal. For instance, themobile device 102 can analyze portions of an acceleration signal andportions of an orientation signal corresponding to measurements obtainedafter an identified impact. Based on the analysis, the mobile device 102can determine whether the impact likely corresponds to signs of traumaand/or impairment in motion.

This analysis can be performed, for example, using an activityclassifier. An activity classifier can be implemented, for example, bycollecting motion data from a sample population after they have fallendown (e.g., acceleration signals and/or orientation signals describingmotion by each individual of the sample population after they fell).Further, information can be collected regarding the condition of each ofthe individuals after he fell (e.g., an indication that certainindividuals exhibited signs of trauma after the fall, an indication thatcertain individuals exhibited impairment in motion after the fall, andso forth). This information can be obtained, for example, from theserver computer system 104 (e.g., a server computer system to collectand aggregate data from multiple different mobile devices). Based onthis information, particular traits or patterns can be determined foreach different type of condition (e.g., signs of trauma, no signs oftrauma, impairment in motion, no impairment in motion, and so forth).Accordingly, when motion data is collected from an additional user, themotion data can be compared against the previously collected motion datato classify that user's condition after he has fallen.

In a similar manner as described above, an activity classifier can beimplemented using various techniques (e.g., decision tree learning,association rule learning, artificial neural networks, deep learning,inductive logic programming, support vector machines clustering,Bayesian networks, reinforcement learning, representation learning,similarity and metric learning, sparse dictionary learning, geneticalgorithms, rule-based machine learning, learning classifier systems,among others).

The mobile device 102 can also determine a user's condition after he hasfallen by identifying one or more motion-based metrics. For example,based on the acceleration signal and/or the orientation signal, themobile device 102 can determine whether the user has taken any stepsafter he has fallen (e.g., by identifying trends or patterns in thesignals representative of the user taking a step, and/or using a sensorsuch as a pedometer). If so, the likelihood that the user is in need ofassistance is lower. Otherwise, the likelihood that the user is in needof assistance is higher.

As another example, based on the acceleration signal and/or theorientation signal, the mobile device 102 can determine whether the userhas stood up after he has fallen (e.g., by identifying trends orpatterns in the signals representative of the user rising from theground and standing). If so, the likelihood that the user is in need ofassistance is lower. Otherwise, the likelihood that the user is in needof assistance is higher.

As another example, the mobile device 102 can determine a duration oftime during which the standard deviation and the magnitude of theacceleration vector was below a threshold value. If the magnitude of theacceleration vector was below the threshold value for a certain periodof time, this may indicate that the user is stationary (e.g., not movinghis body). Further, if the user is stationary after he has fallen, thismay indicate that the user is dazed or injured. Accordingly, thelikelihood that the user is in need of assistance is higher. However, ifthe standard deviation and the magnitude of the acceleration vectorexceeded the threshold value for that period of time, this may indicatethat the user is moving his body after he has fallen (e.g.,. the user isnot stationary). Accordingly, the likelihood that the user is in need ofassistance is lower.

For instance, as shown in FIG. 7, the mobile device 102 can obtain a“raw” acceleration signal 700 (e.g., in a similar manner as describedabove with respect to the acceleration signal 300). In the example shownin FIG. 7, the acceleration signal 700 contains three channels, eachreferring to measured acceleration according to a different directionwith respect to the mobile device 102 (e.g., an x-channel, y-channel,and z-channel). Further, the acceleration signal 700 exhibits a sharpincrease 702 indicative of an impact.

Further, the mobile device 102 can compute the vector magnitude (VM) ofacceleration signal 700 to obtain the vector magnitude signal 704. Inpractice, when the mobile device 102 is static (e.g., not moving), thevector magnitude signal 704 will be 1 (or approximately 1). When themobile device 102 is moving, the vector magnitude signal 704 willdeviate from 1. Therefore, the relationship [VM−1] can be used as ametric of quiescence. Similarly, the standard deviation of theacceleration signal can also be used as a metric of quiescence.

Further, the mobile device 102 can identify the periods of time 706after the impact during which the magnitude of the normalizedacceleration signal 704 is less than a particular threshold value(indicated by darkened rectangles). In some cases, the threshold valuecan be 0.025 g. However, in practice, this threshold value can vary,depending on the implementation.

Further, the mobile device 102 can sum each of the periods of time 706to determine a cumulative duration of time 708 during which themagnitude of the normalized acceleration signal 704 was below athreshold value. If the cumulative duration of time 708 is relativelylarger, the likelihood that the user is in need of assistance is higher.However, if the cumulative duration of time 708 is relatively smaller,the likelihood that the user is in need of assistance is lower.

As another example, based on the acceleration signal and/or theorientation signal, the mobile device 102 can determine the pose angleof the mobile device 102. This pose angle can be determined, forexample, based on the acceleration signal (e.g., a filtered accelerationsignal) and/or the motion orientation of the mobile device 102 (e.g.,information derived from a fusion of acceleration data and gyroscopicdata). A filtered acceleration signal can be, for example, anacceleration signal having one or more channels (e.g., x-channel,y-channel, and/or z-channel), with high-frequency content removed (e.g.,greater than a particular threshold frequency). In some cases, theremoved high-frequency content can correspond to machine-caused motion(e.g., buses, trains, etc.). Based on these sources of information, themobile device 102 can determine which direction the forearm of thewearer is pointing (e.g., approximately the pose angle of the mobiledevice 102 in the x-direction).

If the forearm of the wearer is pointing toward the horizon for a longerperiod of time (e.g., greater than a threshold amount of time), with nomotions below or above the horizon, the user may be more likely to beinjured. For example, this may indicate that the user is lying on thefloor, and is not moving their arm up and down. However, if the user'sforearm is moving above and below the horizon repeatedly, withrelatively large motions, (e.g., 45°), then this may indicate that auser is less impaired, particular if a determination is made that theuser had been ascending or descending stairs.

As another example, based on the acceleration signal and/or theorientation signal, the mobile device 102 can determine the number oftimes that the acceleration signal (e.g., a filtered accelerationsignal) crosses a given threshold value T. This metric can be referredto as the number of threshold-crossings. As above, a filteredacceleration signal can be, for example, an acceleration signal havingone or more, with high-frequency content removed. Symmetric crossingsrequire that the acceleration signal went both above T and below (−T)(or vice-versa), whereas asymmetric crossings count the number of timesthe signal went beyond ±T, regardless of whether it then crossed ±T.

These threshold-crossings indicate human movement. For example, a user'ssteps will typically generate symmetric threshold-crossings, while auser reaching for something or rotating the wrist will typically causeasymmetric crossings, and so forth. By counting the number ofthreshold-crossings, the mobile device 102 can determine whether anindividual is likely to be impaired. For example, the greater number ofthreshold-crossings, the less likely that the user is impaired.

In some cases, this “post-impact” information can be used in conjunctionto determine whether the user may be in need of assistance. As anexample, the mobile device 102 can make a determination regarding auser's condition after he has fallen based on sample data collected froma sample population. For instance, the mobile device can collect motiondata from a sample population (e.g., acceleration signals and/ororientation signals). Further, information can be collected regardingthe condition of each of the individuals (e.g., an indication thatcertain individuals exhibited signs of trauma when the motion data wasmeasured, an indication that certain individuals exhibited impairment inmotion when the motion data was measured, and so forth). Thisinformation can be obtained, for example, from the server computersystem 104 (e.g., a server computer system to collect and aggregate datafrom multiple different mobile devices).

Using this information, one or more correlations can be determinedbetween the characteristics of a user's movement and the condition ofthe user. For example, based on the sample data collected from thesample population, a correlation can be determined between one or moreparticular characteristics of a user's movement corresponding to a userexhibiting signs of trauma. Accordingly, if the mobile device 102determines that the user's motion exhibits similar characteristics, themobile device 102 can determine that the user is likewise exhibitingsigns of trauma. As another example, based on the sample data collectedfrom the sample population, a correlation can be determined between oneor more particular characteristics of a user's movement corresponding toa user exhibiting in impairment in motion. Accordingly, if the mobiledevice 102 determines that the user's motion exhibits similarcharacteristics, the mobile device 102 can determine that the user islikewise exhibiting an impairment in motion.

These correlations can be determined using various techniques. Forexample, in some cases, these correlations can be determined through theuse of “machine learning” techniques such as decision tree learning,association rule learning, artificial neural networks, deep learning,inductive logic programming, support vector machines clustering,Bayesian networks, reinforcement learning, representation learning,similarity and metric learning, sparse dictionary learning, geneticalgorithms, rule-based machine learning, learning classifier systems,among others.

As an example, FIG. 8 shows a decision tree 800 generated using sampledata collected from a sample population (e.g., “trained” using thesample data). In this example, the sample data includes “positivecontrol” information collected from users who were sleeping (e.g.,simulating the behavior of individuals who exhibit signs of trauma orimpairment in motion). The sample data includes “negative control”information collected from unimpaired users who were performing day today physical activities.

As shown in FIG. 8, certain combinations of characteristics areindicative of a user exhibiting signs of trauma and/or impairment inmotion. For example, if (i) the duration of time during which themagnitude of an acceleration signal was low (parameter“duration_low_vm”) is between 48.1 seconds and 51.2 seconds, and (ii)the user took fewer than 0.5 steps (parameter “steps”), a determinationcan be made that the user is exhibiting signs of trauma and/orimpairment in motion.

As another example, if (i) the duration of time during which themagnitude of an acceleration signal was low (parameter“duration_low_vm”) is between 51.2 seconds and 58.9 seconds, (ii) theuser took fewer than 0.5 steps (parameter “steps”), and (iii) the userwas standing for less than 34.7 seconds (parameter “standing”), adetermination can be made that the user is exhibiting signs of traumaand/or impairment in motion. In the example shown in FIG. 8, theduration that a user was standing during the post-fall period (parameter“standing”) is indicated by an integer between 0 and 600, referring tothe duration time in tenths of seconds out of a total sample window of60 seconds. For instance, if the “standing” value is 347, this indicatesthat the user was standing for 34.7 seconds post-fall out of the samplewindow of 60 seconds. In this example, this particular branch indicatesthat “sleep” (the positive control) was likely if the user was standingbetween 0 and 34.7 seconds (e.g., the user was standing for less than orequal to approximately half of the post-fall period). In practice, thisreflects the expectation that a user would not be standing after a fall.

As another example, if (i) the duration of time during which themagnitude of an acceleration signal was low (“duration_low_vm”) isbetween 51.2 seconds and 58.9 seconds, and (ii) the user took greaterthan 0.5 steps (“steps”), a determination can be made that the user isnot exhibiting signs of trauma and/or impairment in motion. Althoughexample decision branches and parameter values are shown in FIG. 8,these are merely illustrative examples. In practice, the decisionbranches and/or parameter values of a decision tree can vary, dependingon the implementation.

In the example described above, the mobile device 102 determines whethera user may be in need of assistance based on the acceleration signal andthe orientation signal. However, the mobile device 102 is not limited toonly using these types of information. For instance, in some cases, themobile device 102 can consider additional information, such as a signaldescribing a heart rate of the user. As an example, the heart rate ofthe user is deviates from a particular range (e.g., a “normal” or“healthy” range), the mobile device 102 may determine that the user ismore likely to be in need of assistance.

In some cases, “pre-impact” information, impact information, and“post-impact” information can be used in conjunction to determinewhether a user has fallen and may be in need of assistance. In somecases, this determination can be made using a decision tree.

As an example, FIG. 9 shows a decision tree 900 for determining thateither (i) a user has fallen and may be in need of assistance (indicatedas “fall” in decision tree 900), or (ii) a user has either not fallen,or has fallen but is not in need of assistance (indicated as “not fall”in decision tree 900). As shown in FIG. 9, certain combinations ofcharacteristics are indicative of each outcome.

For example, if the user experienced a high impact (e.g., greater than afirst threshold value), and exhibited signs of trauma and/or impairmentin motion after the impact, a determination can be made that the userhas fallen and may be in need of assistance. As another example, if theuser experienced a moderate impact (e.g., less than the first thresholdvalue, but greater than a second threshold value smaller than the firstthreshold value), was performing certain activities prior to the impact(e.g., walking, climbing stairs, etc.), was performing a flailing or abracing motion prior to impact, and exhibited signs of trauma and/orimpairment in motion after the impact, a determination can be made thatthe user has fallen and may be in need of assistance. Otherwise, adetermination can be made that the user has either not fallen, or hasfallen but is not in need of assistance. Although example decisionbranches are shown in FIG. 9, these are merely illustrative examples. Inpractice, the decision branches of a decision tree can vary, dependingon the implementation.

In some cases, a fall detector can be a Bayesian classifier. As anexample, the posterior probability of fall can be calculated given a setof features:

p(f all|f1, f2, f3, . . . )=p(f1|f all) p(f2|f call) p(f3|f all) . . .p(f all)/p(f1, f2, f3, . . . ),

where f1, f2, f3, . . . are features computed from acceleration andorientation signals (e.g., an impact magnitude, a jerk, a time to thethird zero-crossing, a pre-impact activity, a presence of bracing orflailing, and/or post-impact features determined from a samplepopulation), and p(f all) is the priori probability of a fall, which canbe determined based on age, gender and/or other biometric informationfrom the sample population. Analogously, the posterior probability of animpact not being a fall can be computed as:

(ADL|f1, f2, f3, . . . )=p(f1|ADL) p(f2|ADL) p(f3|ADL) . . .p(ADL)/p(f1, f2, f3, . . . ),

where ADL represents activities of daily living, which do not containany instances of the user falling. The mobile device 102 can determinethat a fall has occurred when the ratio p(f all|f1, f2, f3, . . .)/p(ADL|f1, f2, f3, . . . ) is greater than a threshold value. Inpractice, the threshold value can vary, depending on the implementation.

Other types of information can also be used to determine whether a userhas fallen. As an example, as discussed above, erratic motions over aperiod of time may be more indicative of a fall (e.g., the user may beflailing his arms while falling). However, periodic motions over aperiod of time may be more indicative of intentional movement by theuser (e.g., periodic movements of the arms while shaking hands, choppingfood, etc.). Accordingly, the periodicity of a user's motion can be usedto determine whether a user has fallen.

In some cases, the periodicity of a user's motion can be determinedbased on a first periodicity metric corresponding to a Fast FourierTransform (FFT) of the coherent energy of the acceleration signal over awindow of time (e.g., 10 seconds) after a detected impact. The coherentenergy is the sum of the peaks in the spectrum whose quality is greaterthan a particular threshold value. A greater first periodicity metriccan indicate greater periodicity of movement, which may correspond to alower likelihood that the user has fallen. A lower first periodicitymetric can indicate lesser periodicity of movement, which may correspondto a higher likelihood that the user has fallen.

In some cases, the periodicity of a user's motion can be determinedbased on a second periodicity metric corresponding to an interquartilerange (IQR) of the inter-peak interval of the acceleration signal over awindow of time (e.g., 10 seconds) after a detected impact. This can becalculated by identifying all of the peaks around the detected impactthat are greater than a particular threshold value, then calculating theIQR of the intervals between adjacent peaks. If the repeating peaks arespaced equally (or substantially equally) apart, the IQR will be small,indicating periodic movement. Accordingly, a lower second periodicitymetric can indicate greater periodicity of movement, which maycorrespond to a lower likelihood that the user has fallen. In contrast,a greater second periodicity metric can indicate lesser periodicity ofmovement, which may correspond to a higher likelihood that the user hasfallen.

In some cases, the mobile device 102 can be used to distinguish betweendifferent types of falls. For example, a mobile device 102 candistinguish between a tripping fall (e.g., a fall in which a userstumbles forward, such as when his feet are caught by an obstruction), aslipping fall (e.g., a fall in which a user falls backwards, such aswhen he loses his balance on a slippery surface), a tumbling fall (e.g.,a fall in which a user has rotated or rolled about an axis of rotation,such as when a user rolls down a hill or flight of stairs), and/or othertypes of falls. This can be useful, for example, as different types offalls may have different effects on a user (e.g., some may be moreinjurious to a user, while others may be less so). Thus, the mobiledevice 102 can take a more specific action in response to the particularnature of the user's fall.

As described herein, a mobile device can determine whether a user hasfallen based, at least in part, on the mobile device's “pose angle,” ororientation with respect to the inertial frame (e.g., the earth frame).In some cases, this can be determined using information obtained by anaccelerometer (e.g., by using an acceleration signal generated by theaccelerometer to determine to identify the direction of gravity, or theinertial z-direction) and/or information from an orientation sensor,such as a gyroscope (e.g., by using an orientation signal generated bythe gyroscope to determine the orientation of the device with respect tothe direction of gravity).

In some cases, a mobile device can distinguish between tripping fallsand slipping falls based on changes in the pose angle of the mobiledevice prior to and/or after an impact, and determining whether themeasured changes in the pose angle are indicative of a trip or a fall.As an example, when a user trips, he often moves his arm downward tobrace for impact. Accordingly, if the mobile device is attached to theuser's wrist, it may experience a negative change in pose angle beforethe impact. Further, at the time of the fall, the pose angle may bepointing towards the ground. As another example, when a user slips, heoften throws his arms upward in an attempt to regain his balance.Accordingly, if the mobile device is attached to the user's wrist, itmay experience a positive change in pose angle before the impact.

These characteristics can be used to distinguish tripping falls fromslipping falls. For example, FIG. 10 shows a scatter plot 1000 of poseangle data collected from a sample population of users that experienceddifferent types of falls using a mobile device attached to each user'swrist. Each point 1002 indicates a particular type of fall (e.g., atripping fall, a slipping fall, or other types of falls), thecorresponding pose angle change (e.g., in degrees) of the mobile deviceprior to the impact (indicated in the x-axis), and the correspondingpose angle change of the mobile device after the impact (indicated inthe y-axis). The sign of the pose angle change can indicate thedirection of motion. For example, a positive pose angle change canindicate the final pose angle of the mobile device is higher than theinitial pose angle during the time-window of consideration. Likewise, anegative value of pose angle change can indicate that the final poseangle of the mobile device is lower than the initial pose angle. Asshown in FIG. 10, when a user experiences a tripping fall, the mobiledevice moves in the downward direction prior to impact as the user ispreparing to brace, and then moves upwards after hitting the surface.Accordingly, trip falls often have a negative pose angle change prior toimpact and a positive pose angle change after the impact. In contrast,when a user experiences a slipping fall, the mobile device moves in theupward direction as the user flails his arm before impact, and thenmoves downwards after impact. Accordingly, slip falls often have apositive pose angle change before impact and a negative pose anglechange after the impact. Thus, a mobile device can distinguish betweentripping falls and slipping falls based on changes in the pose angle ofthe mobile device prior to and/or after an impact, and determiningwhether the measured changes in the pose angle are indicative of a tripor a fall. Further, if the magnitude of pose angle change in eitherdirection is not large enough to convincingly suggest a trip orslip-like fall behavior, the fall may be categorized into a hold-allcategory of “other” falls. For example, this may be likely in case offalls where the user faints due to dehydration, where there may not be apronounced flailing or bracing motion before impact as the user losesconsciousness

In some cases, a system can determine whether a user has tumbled (e.g.,fallen in such a way that the user has rotated or rolled about an axisof rotation). This can be beneficial, for example, as tumbling falls canbe particularly injurious to a user, and can correspond to higherlikelihood that a user is in need of assistance. Example tumbling fallsinclude a user falling and rolling down a flight of stairs or a hill, auser falling headlong (e.g., partially or fully somersaulting), a user“logrolling,” or another fall involving a certain degree of rolling orrotation.

As an example, as described above, a user 110 can position the mobiledevice 102 on his body, and go about his daily life (e.g., walk, run,sit, lay down, participate in a sport or athletic activity, or any otherphysical activity). During this time, the mobile device 102 collectssensor data regarding the motion of the user 110. For instance, usingthe motion sensors 2510 shown in FIG. 25 (e.g., one or moreaccelerometers), the mobile device 102 can measure an accelerationexperienced by the motion sensors 2510, and correspondingly, theacceleration experienced by the mobile device 102. Further, using themotion sensors 2510 (e.g., one or more compasses or gyroscopes), themobile device 102 can measure an orientation of the mobile device 102.In some cases, the motion sensors 2510 can collect data continuously orperiodically over a period of time or in response to a trigger event.Further, the mobile device 102 can determine the acceleration and/ororientation with respect to a frame of reference fixed to the mobiledevice 102 (e.g., a body frame) and/or with respect to the inertialframe (e.g., the earth frame).

The mobile device 102 can continuously measure the acceleration and theorientation of the mobile device over a sliding sample window (e.g., togenerate a continuous sample buffer). The acceleration signal can beused to identify the direction of gravity (or the inertial z-direction),and the orientation signal can be used to determine the orientation ofthe mobile device 102 with respect to the direction of gravity. Usingthis information, the mobile device 102 can determine the pose angle ofthe mobile device 102 (approximating the orientation of the user 110).Further, using this information, the rate of rotation of the mobiledevice 102 can be determined with respect to both the body frame and theinertia frame (approximating the rate of rotation of the user 110 withrespect to the body frame and the inertia frame).

As examples, plot 1100 in FIG. 11A shows acceleration signals 1102obtained over a sliding sample window spanning approximately 2.5 secondsto 7.5 seconds, and plot 1104 in FIG. 11B shows a corresponding rate ofrotation signals 1106 with respect to the inertia frame over the samesliding sample window. In this example, a user has fallen on a flight ofstairs at approximately the 3 second mark, contacted the stairs and/orrailings approximately between the 3 second and 6.5 second marks, andbegan rolling down the stairs at 6.5 second mark. Plot 1100 includesthree different acceleration signals 1102, each corresponding to adifferent direction (e.g., an x-direction, y-direction, and z-directionin a Cartesian coordinate system in a frame of reference fixed to themobile device 102, or the body frame). Similarly, plot 1104 includesthree different rate of rotation signals 1106, each corresponding to adifferent direction (e.g., an x-direction, y-direction, and z-directionin a Cartesian coordinate system fixed to the inertial frame). Althougha sliding sample window having an example length is shown, in practice,a sliding sample window can have any length (e.g., 1 second, 2 seconds,3 seconds, 4 seconds, or any other length of time). Further, differentframes of references can be used other than Cartesian coordinate system.For example, in some cases, a quaternion coordinate system can be used.

Further, using this information, the mobile device 102 can determine oneor more instantaneous axes of rotation of the mobile device 102 over thesliding sample window, the average axis of rotation of the mobile deviceover the sliding sample window, and a degree of uncertainty associatedwith the average axis of rotation (e.g., a variance value, standarddeviation value, or other uncertainty metric). As an example, plot 1108in FIG. 11C shows an axis of rotation signal 1110 indicating theinstantaneous axis of rotation of the mobile device 102 with respect tothe inertial frame for any given point in time in the sliding samplewindow. An angle of 0° indicates that the mobile device 102 is rotatingalong an axis parallel to the direction of gravity at a particular pointin time, while an angle of 90° indicates that the mobile device isrotating along an axis perpendicular to the direction of gravity at aparticular point in time. The average axis of rotation of the mobiledevice over the sliding sample window and the degree of uncertaintyassociated with the average axis of rotation can be determined usingthis signal (e.g., by averaging the values of the signal, anddetermining a variance, standard deviation, or other uncertainty metricbased on the values of the signal).

Further, the mobile device 102 can determine whether the mobile device102 is rotating with respect to a consistent axis of rotation over thesliding sample window. As an example, if the variation or deviationbetween the one or more instantaneous axes of rotation of the mobiledevice 102 and the average axis of rotation during the sliding samplewindow is lower, the mobile device 102 can determine that the mobiledevice 102 is rotating more consistently about a particular axis ofrotation. However, if the variation or deviation between the one or moreinstantaneous axes of rotation of the mobile device 102 and the averageaxis of rotation during the sliding sample window is higher, the mobiledevice 102 can determine that the mobile device 102 is rotating lessconsistently about a particular axis of rotation. In some cases, themobile device 102 can determine that it is rotating with respect to aconsistent axis of rotation over the sliding sample window if thevariation or deviation between the one or more instantaneous axes ofrotation of the mobile device 102 and the average axis of rotationduring the sliding sample window is lower than a particular thresholdvalue.

If the mobile device 102 determines that mobile device 102 is rotatingwith respect to a consistent axis of rotation over the sliding samplewindow, the mobile device 102 can determine an angular displacement ofthe mobile device 102 with respect to the sliding sample window. Thiscan be performed, for example, by performing an angular integration ofthe rate of rotation signal with respect to the inertial frame over thesliding sample window. As an example, plot 1112 in FIG. 11D shows atotal angular displacement signal 1014 corresponding to the totalangular displacement of the mobile device 102 with respect to theinertial frame over the sliding period of time. The total angulardisplacement signal 1014 can be obtained, for example, by integratingone or more of the rate of rotation signals 1006 shown in FIG. 11B on asliding basis.

The mobile device 102 can determine whether a user has tumbled based onthe total angular displacement, and the instantaneous axis of rotationof the mobile device 102. For example, as shown in FIG. 11C, theinstantaneous axis of rotation of mobile device 102 is relatively stablefrom around the 6.5 second mark onward (corresponding to the time inwhich the user began rolling down the stairs). Further, the axis ofrotation is consistently approximately 90° during this time, indicatingthat the user was rolling along an axis of rotation approximatelyperpendicular to the direction of gravity. Further, as shown in FIG.11D, the mobile device 102 experienced a relatively large angulardisplacement at around the same time period. The combination of thesecharacteristics can be indicative of a tumbling fall.

In some cases, the mobile device 102 can determine that a user hastumbled if the one or more instantaneous axes of rotation are greaterthan a threshold angular value and/or and if the angular displacement isgreater than a threshold displacement value. In some cases, the mobiledevice 102 can determine that a user has tumbled if the one or moreinstantaneous axes of rotation are relatively large (e.g., greater thanthreshold angular value) and/or consistent over a threshold period oftime (e.g., approximately 90° over a period of 1 second, 2 seconds, 3seconds, or some period of time).

In some cases, the mobile device 102 can also identify different typesof tumbling falls. For example, if the one or more instantaneous axes ofrotation are approximately 90° with respect to the direction of gravity,this can signify a tumble in which a user rolls headlong or sideways(e.g., somersaults or logrolls). As another example, if the one or moreinstantaneous axes of rotation is between 0° and 90° with respect to thedirection of gravity, this can signify a tumble in which a user twistswhile falling.

In a similar manner as described above, upon identifying that a user hastumbled and may be in need of assistance, the mobile device 102 canautomatically take an appropriate action in response. For instance, themobile device 102 can automatically notify the user or others (e.g., anemergency response system, an emergency contact, or others) regardingthe situation. Similarly, in some cases, upon determining that the userhas tumble and may be in need of assistance, the mobile device 102 canfirst notify the user before transmitting messages to others (e.g.,before transmitting a notification to the emergency response system, theemergency contact, or others).

The implementations described herein are not limited solely to detectingtumbling falls by a user. In some cases, one or more implementations canbe used to determine intentional tumbles or rotations by a user. As anexample, a device can use rotational data to determine whether a userhas somersaulted or logrolled (e.g., as a part of an athletic activity,such as gymnastics). As another example, a device can use rotationaldata to determine whether a user has performed a tumble turn duringswimming (e.g., to count the number of laps that the user has performedin a swimming pool). In practice, other applications are also possible,depending on the implementation.

Upon identifying that a user has fallen and may be in need ofassistance, the mobile device 102 can automatically take an appropriateaction in response. For instance, the mobile device 102 can determinethat the user has fallen and may be in need of assistance, and inresponse, automatically notify the user or others regarding thesituation. As an example, the mobile device 102 can display anotification to the user to inform the user that he has fallen and maybe in need of assistance. Further, the mobile device 102 can transmit anotification to a remote device (e.g., one or more of the servercomputer system 104 and/or communication devices 106) to inform othersof the user's condition. This can include, for example, notifications toan emergency response system, a computer system associated with medicalpersonnel, a computer system associated with a caretaker of the user, abystander, etc. Notifications can include, for example, auditoryinformation (e.g., sounds), textual information, graphical information(e.g., images, colors, patterns, etc.), and/or tactile information(e.g., vibrations). In some cases, notification can be transmitted inthe form of an e-mail, instant chat message, text message (e.g., shortmessage service [SMS] message), telephone message, fax message, radiomessage, audio message, video message, haptic message (e.g., one or morebumps or vibrations), or another message for conveying information.

In some cases, the mobile device 102 can transmit a message to anothersystem using pre-determined contact information. For example, the userof the mobile device 102 can provide contact information regarding anemergency contact, such a telephone number, an e-mail address, a username in an instant chat service, or some other contact information. Themobile device 102 can generate a message having a compatible data format(e.g., an audio telephone message, a video telephone message, a textmessage, an e-mail message, chat message, or some other message), andtransmit the message to emergency contact using the provided contactinformation (e.g., using one or more of the server computer system 104and/or the communication devices 106).

In some cases, upon determining that the user has fallen and may be inneed of assistance, the mobile device 102 can first notify the userbefore transmitting messages to others (e.g., before transmitting anotification to an emergency response system, an emergency contact, orothers). In response, the user can instruct the mobile device 102 not tonotify others (e.g., if the user is not in need of assistance).Alternatively, the user can expressly instruct the mobile device 102 tonotify others (e.g., if the user is in need of assistance). In somecases, if the user does not respond to the notification, the mobiledevice 102 can automatically notify others (e.g., in the event that theuser is incapacitated and unable to respond). In some cases, the mobiledevice 102 can notify the user multiple different times, and if noresponse is received from the user after a period of time (e.g., 25seconds, or some other period of time), automatically notify others forassistance.

In some cases, the mobile device 102 can determine whether thenotification others using a state machine. A state machine can specifythat the mobile device send a fall alert if it observes a short periodof quiescence, a behavior that often occurs after a fall. Then, themobile device detects incapacity by checking for a period of “long lie”by the user (e.g., a period of time in which the user does not move orarise). If incapacity is detected, the mobile device can automaticallytransmit a distress call to a third party and/or instruct another deviceto transmit the distress call). The user can also cancel the distresscall (e.g., if the user believes that he does not require assistance).

An example state machine 1200 is shown in FIG. 12. In this example, themobile device begins in a “nominal” state 1202 (e.g., a low alert stateof the mobile device), a “likely fall” state 1204 (e.g., an elevatedalert state of the mobile device upon detection of a possible fall bythe user), “wait” states 1206 a and 1206 b (e.g., states in which themobile device waits for additional information, such as a potentialinput by a user or movement by the user), “alert” states 1208 a and 1208b (e.g., states in which the mobile device alerts the user of the mobiledevice regarding a possible fall and the possibility of sending adistress call to a third-party), a “cancel” state 1210 (e.g., a state inwhich an impending distress call is canceled), and an “SOS” state 1212(e.g., a state in which a distress call is conducted). The mobile devicecan transition between each of the states based on the detection ofcertain signatures of a fall (e.g., as described herein), detectedperiods of quiescence (e.g., a lack of movement by the user), and/orinputs by the user.

As an example, the mobile device begins at the “nominal” state 1202.Upon detection of a fall signature (e.g., a combination of sensormeasurements and other information indicative of a fall), the mobiledevice transitions of the “likely fall” state 1204. Upon detection of aperiod of quiescence T_(Q) after the fall, the mobile device transitionsto the “alert” state 1208 b, and alerts the user of the detected falland informs the user of the possibility of sending a distress call to athird-party (e.g., an emergency responder). The mobile devicetransitions to the “wait” state 1206 b and awaits possible movements bythe user. If no user movement is detected during a “timeout” period oftime after the transmission of the fall alert elapses (e.g., 30 seconds)and a continuous period of quiescence (e.g., T_(LL)=10 seconds) isdetected during this time, the mobile device transitions to the “SOS”state 1212 and transmits the distress call. The mobile device thenreturns to the “nominal” state 1202. This can be useful, for example, ifthe user has fallen and becomes incapacitated for a lengthy period oftime. The mobile device can automatically summon help for the user, evenwithout the user's input.

As another example, the mobile device begins at the “nominal” state1202. Upon detection of a fall signature, the mobile device transitionsof the “likely fall” state 1204. Upon detection of a period ofquiescence T_(Q) after the fall, the mobile device transitions to the“alert” state 1208 b, and alerts the user of the detected fall andinforms the user of the possibility of sending a distress call to athird-party. The mobile device transitions to the “wait” state 1206 band awaits possible movements by the user. If user movement is detectedbefore a “timeout” period of time after the transmission of the fallalert elapses (e.g., 30 seconds), the mobile device transitions to the“cancel” state 1210, and cancels the distress call. The mobile devicethen returns to the “nominal” state 1202. This can be useful, forexample, if the user has fallen, but affirmatively indicates that hedoes not require assistance. The mobile device refrain fromautomatically summon help for the user under this circumstance.

As another example, the mobile device begins at the “nominal” state1202. Upon detection of a fall signature, the mobile device transitionsof the “likely fall” state 1204. Upon detection of certain types ofmovements after the fall (e.g., stepping movements, standing movements,high dynamic movement, or any other movement indicative of recovery bythe user), the mobile device transitions to the “waiting” state 1206 a.Upon detection of a period of quiescence T_(Q) after the cessation ofmovement by the user, the mobile device transitions to the “alert” state1208 a, and alerts the user of the detected fall. The mobile devicetransitions to the “cancel” state 1210, and does not transmit a distresscall. The mobile device then returns to the “nominal” state 1202. Thiscan be useful, for example, if the user has fallen, but exhibits signsof recovery. The mobile device can alert the user regarding the fall,but does not automatically summon help for the user under thiscircumstance.

As another example, the mobile device begins at the “nominal” state1202. Upon detection of a fall signature, the mobile device transitionsof the “likely fall” state 1204. Upon detection of certain types ofmovements after the fall (e.g., stepping movements, standing movements,high dynamic movement, or any other movement indicative of recovery bythe user), the mobile device transitions to the “waiting” state 1206 a.Upon the passage of 25 seconds without the detection of a period ofquiescence T_(Q), the mobile device returns to the “nominal” state 1202.The mobile device then returns to the “nominal” state 1202. This can beuseful, for example, if the user has fallen, but exhibits signs ofrecovery and continues moving for a lengthy period of time after thefall. The mobile device can refrain from alerting the user orautomatically summoning help for the user under this circumstance.

Although time values are shown in FIG. 12, there are merely illustrativeexamples. In practice, one or more of the time values can differ,depending on the implementation. In some cases, one or more of the timevalues can be tunable parameters (e.g., parameters that are selectedempirically to distinguish between different types or events orconditions).

In some cases, the response sensitivity of the mobile device 102 canvary depending on the characteristics of the user. For instance, themobile device 102 can determine a probability that the user has fallenand may be in need of assistance. If the probability is greater than athreshold level, the mobile device can automatically notify the userand/or others of the user's fall. The threshold level can vary based oneach particular user. For example, if the user is at a higher risk offalling, the threshold level can be lower (e.g., such that the mobiledevice 102 is more likely to notify the user and/or others about afall). However, if the user is at a lower risk of falling, the thresholdlevel can be higher (e.g., such that the mobile device 102 is lesslikely to notify the user and/or others about a fall). The thresholdlevel for each user can be varied based on one or more behavior and/ordemographic characteristics of the user, such as the user's age,activity level, walking speed (e.g., maximum observed waling speed overa period of time), or other factors.

As an example, FIG. 13 shows a schematic representation 1300 of a falldetection technique based on user-specific sensitivity. The mobiledevice receives motion data 1302, and computes behavioral features ofthe user's motion (e.g., flailing motions, bracing motions, chaoticmotions, periodic motions, or other features, as described herein)(1304). Further, the mobile device determines sensitivity thresholdsbased on the user's behavior and/or demographic characteristics (1306).The mobile device determines whether the user has fallen and/or whetherto transmit a distress call based on the determined features andthresholds (e.g., using a fall detector 1308 performing one or more ofthe techniques described herein).

As described herein, a mobile device can determine whether a user hasfallen based, at least in part, on the mobile device's “pose angle,” ororientation with respect to the inertial frame. In some cases, this canbe determined using information obtained by an accelerometer and/orinformation from an orientation sensor, such as a gyroscope.

In practice, the dynamic range of the accelerometer can vary, dependingon the implementation. For example, the accelerometer can have a dynamicrange of 16 g. As another example, the accelerometer can have a dynamicrange of 32 g (e.g., to detect a greater range of accelerations).

In some cases, the accelerometer and the gyroscope can each obtainmeasurements according to the same sample rate (e.g., 200 Hz, 400 Hz,800 Hz, or some other frequency). In some cases, the accelerometer andgyroscope can each obtain measurements according to different samplerates. As an example, the accelerometer can obtain measurementsaccording to a higher sample rate (e.g., 800 Hz), while the gyroscopecan obtain measurements according to a lower sample rate (e.g., 200 Hz).This can be useful, for example, in selectively reducing the powerconsumption of one sensor (e.g., a sensor that consumes more powerduring operation) relative to the other to improve the power efficiencyof the mobile device. In some cases, the sample rate of theaccelerometer and/or gyroscope can be dynamically adjusted duringoperation. For instance, the sample rate of the accelerometer and/orgyroscope can be selectively increased during certain periods of timeand/or in response to certain conditions (e.g., greater user motion),and decreased during certain other periods of time and/or in response tocertain other conditions (e.g., lesser user motion).

In some cases, one of the accelerometer or the gyroscope can be used toobtain measurements, while the other sensor is disabled (e.g., such thatit does not collect measurements). The disabled sensor can beselectively activated in based on measurements obtained from the activesensor. This can be useful, for example, in reducing the powerconsumption of the mobile device (e.g., by operating only one of theaccelerometer or the gyroscope during certain periods of time, andselectively operating both of the accelerometer or the gyroscope inresponse to certain conditions).

As an example, the mobile device can disable the gyroscope, and use theaccelerometer to obtain acceleration measurements over a period of time.If the measured acceleration is greater than a particular thresholdlevel (e.g., the root mean square [RMS] energy of the accelerometer'sacceleration signal is greater than a threshold energy level), themobile device can activate the gyroscope and collect orientationinformation. Accordingly, the gyroscope is selectively activated inresponse to the detection of “significant” motion by the accelerometer.

In some cases, the gyroscope can be disabled if significant motion is nolonger detected. For example, if the RMS energy of the accelerometer'sacceleration signal is less than the threshold energy level for aparticular period of time (e.g., a pre-defined time interval), themobile device can disable the gyroscope, and continue operating theaccelerometer. In some cases, the gyroscope can be disabled a particularperiod of time after the gyroscope was activated (e.g., after apre-defined time interval has elapsed since the gyroscope was switchedon). In some cases, the gyroscope can be disabled if the RMS energy ofthe accelerometer's acceleration signal is less than the thresholdenergy level for a first time interval, or if a second time interval haselapsed since it was activated, whichever occurs first. In practice, thetime intervals can vary, depending on the implementation.

In some cases, the gyroscope can be selectively enabled and disabledaccording to a state machine. FIG. 14 shows an example state machineinclude a “gyro off” state 1402 (corresponding to a disabled state ofthe gyroscope), a “gyro on” state 1404 (corresponding to an enabledstate of the gyroscope), and a “wait” state 1406 (corresponding to astate in which the mobile device waits further information beforeadjusting the operation of the gyroscope).”

The mobile device begins in the “gyro off” state 1402, in which thegyroscope is disabled. The mobile device transitions of the “gyro on”state 1404 and enables the gyroscope upon detection of a period ofnon-quiescence based on an acceleration signal obtained from anaccelerometer. Upon detection of quiescence and low rotation based onthe acceleration signal and an orientation signal from the gyroscope,the mobile device transitions to the “wait” state 1406. If quiescenceand low rotation continues, the mobile device periodically increments acounter over time. If the counter exceeds a threshold value, the mobiledevice returns to the “gyro off” state 1402 and disables the gyroscope,and resets the counter. However, if non-quiescence and/or a sufficientlyhigh degree of rotation is detected, the mobile device instead returnsto the “gyro on” state 1404 and keeps the gyroscope enabled, and resetsthe counter. In this manner, the mobile device selectively enables thegyroscope in response to the detection of movement, and disables itafter a period of quiescence and low rotation.

In some cases, the “Quiescence” condition shown in FIG. 14 can be aBoolean value that is true when the following equation is satisfied:

thr1≤k1*VM+k2*dVM<thr2

where VM is the magnitude of the acceleration signal, dVM is the rate ofchange of the magnitude of the acceleration signal, and thr1 and thr2are tunable threshold values

In some cases, the “Quiescence && Low Rotation” condition shown in FIG.14 can be a Boolean value that is true when the following equation issatisfied:

(thr1+δ≤k1*VM+k2*dVM<thr2−δ) AND (rot<thr3)

where VM is the magnitude of the acceleration signal, dVM is the rate ofchange of the magnitude of the acceleration signal, rot is the rotationrate (e.g., as determined based on the gyroscope), δ is a tunable offsetvalue, and thr1, thr2, and thr3 are tunable threshold values.

In some cases, falls can be detected based on a combination or “fusion”of multiple different types of sensor measurements. For instance, fallscan be detected based on acceleration measurements (e.g., obtained by anaccelerometer), orientation measurements (e.g., obtained by agyroscope), air pressure measurements (e.g., obtained by a pressuresensor or barometer), altitude measurements (e.g., obtained by analtimeter, pressure sensor, accelerometer, or other sensor), heart ratemeasurements (e.g., obtained by a heart rate sensor), and/or other typesof measurements.

As an example, FIG. 15 shows a schematic representation 1500 of a falldetection technique based on multiple types of sensor measurements. Anaccelerometer is used to detect hard impacts (step 1502). Hard impactscan be detected, for example, based on the magnitude of accelerationa_(mag) and the magnitude of jerk j_(mag) measured by the accelerometerover a sliding window. The magnitude of acceleration over the slidingwindow can be calculated using the following equation:

a _(mag)=sqrt(max(|x|)²+max(|y|)²+max(|z|)²)

where x, y, and z are the x, y, and z components of the accelerationsignal, respectively, and max is taken in a 0.2 second window.

The magnitude of jerk over the sliding window can be calculated usingthe following equation:

j _(mag)=max(sqrt(dx ² +dy ² +dz ²))

where dx, dy, and dz are the derivatives of the x, y, and z componentsof the acceleration signal, respectively and max is taken in a 0.2second window.

If a_(mag) is greater than a threshold value thr1 and j_(mag) is greaterthan a threshold value thr2, the mobile device obtains gyroscopicmeasurements (stop 1504), elevation or altitude measurements (step1506), and heartrate information (step 1508), and determines whether afall has occurred based on the measurements (step 1510).

As an example, accelerometer and gyroscopic measurements can be used todetermine an impact direction and the pose angle of the mobile devicebefore, during, and/or after an impact (e.g., as described herein). Insome cases, accelerometer measurements can be used to approximate themobile device's elevation or altitude (e.g., when the mobile device isstatic). In some cases, accelerometer and gyroscopic measurements can beused in conjunction to approximate the mobile device's elevation oraltitude (e.g., when the mobile device is in motion).

As another example, pressure sensors can be used to detect multi-levelfalls (e.g., a user falling off a ladder). As another example, heartrate sensors can be used to detect changes in heart rate, such as anelevation of heart rate (e.g., due of a fight-or-flight response) orheart rate decay curves (e.g., a person's heart rate decay after a fallmay have distinctive characteristics, such as a smaller time constant,compared to the heart rate decay after the end of a physical work out).As another example, accelerometers can be used to detect evaluation oraltitude (e.g., when the device is static). As another example,accelerometers can be used to detect evaluation or altitude (e.g., whenthe device is static).

FIG. 16 shows an example use of an accelerometer and gyroscope inconjunction to determine information regarding the motion of a user(e.g., as a part of step 1504 shown in FIG. 15). The accelerometer andthe gyroscope generate accelerometer and gyroscopic measurements (step1602). Based on this information, a gyroscopic controller canselectively turn off the gyroscope during certain periods of time (e.g.,as described with respect to FIG. 14) (step 1604). The accelerometer andgyroscopic measurements are used in conjunction (e.g., “fused”) toobtain information regarding the device, such as the altitude orelevation of the device (step 1606). Further, this information can beused to determine other information about the device, such as the poseangle and direction from which an impact is experienced by the mobiledevice (step 1608). The mobile device determines whether a fall hasoccurred based on the measurements (step 1610).

An example fall classifier 1700 is shown in FIG. 17. The fall classifier1700 can be used to determine whether a user has fallen, and if so, thetype or nature of the fall. The fall classifier 1700 can be implemented,for example, using the mobile device 102 and/or the system 100 shown inFIG. 1.

The fall classifier 1700 receives inputs indicating the measured motionof the user, and outputs information indicating whether the user hasfallen, and if so, the type or nature of the fall. For instance, asshown in FIG. 1700, the fall classier receives acceleration data 1702indicating an acceleration experienced by a mobile device worn by a user(e.g., measured using an accelerometer), and gyroscopic data 1704indicating an orientation of the mobile device (e.g., measured using agyroscope).

The acceleration data 1702 and gyroscopic data 1704 are combined or“fused” together by a sensor fusion module 1706 (e.g., using one or moreof the techniques described herein), and are considered in conjunctionby the fall classifier 1700. In some cases, the acceleration data 1702and the gyroscopic data 1704 can be combined with respect to one or morespatial axes (e.g., six).

The acceleration data 1702 and the gyroscopic data 1704 can be used inconjunction to determine information regarding the motioncharacteristics of the user. As an example, this data can be used todetermine the altitude 1708 of the mobile device.

Further, the acceleration data 1702 and the gyroscopic data 1704 datacan be input into a feature extraction module 1710, which identifies oneor more features or characteristics of the acceleration data 1702 andthe gyroscopic data 1704. The feature extraction module 1710 can performone or more of the techniques described herein. As an example, thefeature extractor 1710 can determine a wrist angle 1728 of the user(e.g., by determining a pose angle of the mobile device as it is worn bythe user on his wrist).

Further, a behavior of the user can be determined using a behavioralmodeling module 1712. The behavior of a user can be modeling one or moreof the techniques described herein. As an example, based on changes ofthe pose angle of the mobile device 102, the behavioral modeling module1712 can determine behavioral information 1714, such as whether the useris performing a bracing motion (e.g., thrusting his arms out to arrestforward momentum), a balancing motion (e.g., throwing his arms out toregain balance), a flailing motion (e.g., fluttering his arms during andafter an impact), or another other motion. In some cases, a bracingmotion can be detected based on features such as the wrist traversing anegative arc length before impact, and the wrist pointing toward theground at the moment of impact. In some cases, a balancing motion can bedetected based on features such as the wrist traversing a positive arclength as the user attends to regain balance. In some cases, a failingmotion can be detected based on features such as the wrist making a oneor more rapid reversals in motion, either as part of a grasping reflex,or due to repeated secondary impacts with the ground. The behavioralinformation 1714 can be input into a classification module 1716 to aidin the detection and classification of falls.

The fall classifier 1700 can also analyze aspects of the accelerationdata 1702 separately from the gyroscopic data 1704. For example, theacceleration data 1702 can be input into a feature extraction module1718, which identifies one or more features or characteristics of theacceleration data 1702. The feature extraction module 1718 can performone or more of the techniques described herein. As an example, thefeature extractor 1718 can determine impact information 1720, such asthe magnitude of an impact experienced by the user, motions made by theuser prior to the impact, and motions made by the user after the impact.As another example, the feature extractor 1718 can determine a degree ofchaos in the user's motions over a period of time.

The impact information 1720 can be input into an impact detector 1722,which determine if the user actually experienced an impact, and if so,the type or nature of the impact. The impact detector 1722 can performone or more of the techniques described herein. As an example, theimpact detector 1722 can output an indication 1724 regarding whether theuser experienced an impact.

Information from the impact detector 1722 can the behavioral modelingmodule 1712 can be used to determine whether the user has fallen, and ifso, the type or nature of the fall. As an example, based on inputs froma the impact detector 1722 and the behavioral modeling module 1712, theclassification module 1716 can determine that the user has slipped,tripped, rolled, or experienced some other type of fall. As anotherexample, based on inputs from the impact detector 1722 and thebehavioral modeling module 1712, the classification module 1716 candetermine that the user has experienced an impact, but has not fallen.As another example, based on inputs from the impact detector 1722 andthe behavioral modeling module 1712, the classification module 1716 candetermine that the user has fallen, but has recovered. Theclassification module 1716 outputs fall information 1726 indicatingwhether the user has fallen, and if so, the type or nature of the fall.

As described above, multiple types of sensor measurements can be used inconjunction to determine a user's motion characteristics. As an example,FIG. 18 shows a fall sensor fusion module 1800 used to determine whethera user has fallen, and if so, the type or nature of the fall. The sensorfusion module 1800 can be implemented, for example, using the mobiledevice 102 and/or the system 100 shown in FIG. 1.

The fall sensor fusion module 1800 receives inputs from severaldifferent sensors. For example, the fall sensor fusion module 1800receives acceleration data 1802 a indicating an acceleration experiencedby a mobile device worn by a user (e.g., measured using anaccelerometer), and gyroscopic data 1802 b indicating an orientation ofthe mobile device (e.g., measured using a gyroscope). As anotherexample, the sensor fusion module 1800 receives location data 1802 cindicating a location of the mobile device (e.g., measured using aGlobal Navigation Satellite System receiver, such as the Global PositionSystem receiver, and/or a wireless transceiver, such as a Wi-Fi radio.As another example, the sensor fusion module 1800 receives altitude data1802 d indicating an altitude or elevation of the device (e.g., measuredusing an altimeter, barometer, or other altitude sensor). As anotherexample, the sensor fusion module 1800 receives heart rate data 1802 dindicating a heart rate of a user wearing the mobile device (e.g.,measured using a heart rate sensor).

As described herein, the accelerometer data 1802 a and the gyroscopicdata 1802 b can be used to determine whether a user has fallen. Forexample, the accelerometer data 1802 a and the gyroscopic data 1802 bcan be input into a fall classifier 1804. In general, the fallclassifier 1804 can function in a similar manner as described withrespect to FIG. 17. For example, the fall classifier 1804 can determineone or more features based on the acceleration data 1802 a, anddetermine whether the user experienced an impact based on thosefeatures. Further, the fall classifier 1804 can determine one or morefeatures based on both the acceleration data 1802 a and the gyroscopicdata 1802 b, and model a behavior of the user based on the features.Further, the fall classifier 1804 can determine whether a user hasfallen based on the detected impacts and/or the modeled behavior.

Further, the fall classifier 1804 can determine whether a user hasfallen based, at least in part, on the location data 1802 c. Forexample, the location data 1802 c can be input into a threshold module1806. The threshold module 1806 determines information regarding thelocation of the mobile device 1806. For example, the threshold module1806 can determine whether the mobile device is at a user's home, at auser's place of work, at a public area (e.g., a store, gym, swimmingpool, etc.), or some other location. As another example, the thresholdmodule 1806 an determine whether the mobile device is being worn by theuser while the user is driving, biking, skating, skateboarding, ortraveling using some other mode of transportation. This information canbe input into the fall classifier 1804 to improve the detection offalls. For example, a user may be more likely to fall while he is athome, rather than traveling in a car. Accordingly, the fall classifier1804 can increase the sensitivity with which is detects falls upondetermining that the user is at home, versus when the user is driving acar. As another example, a user may be more likely to fall while he isoutside while it is snowing or raining, rather than when it is notsnowing or raining. Accordingly, the fall classifier 1804 can increasethe sensitivity with which is detects falls upon determining that theuser is outside and determining that rain or snow is occurring at thelocation (e.g., based on information obtained from a weather service),versus when it is not raining or snowing at the user's location.

The fall classifier 1804 outputs fall data 1808 indicating whether theuser has experienced a fall, and if so, the type or nature of the fall.The fall data 1808 can be input into a fusion module 1810, which rejectsfalse positives by the fall classifier 1804. For example, the fusionmodule 1810 can receive fall data 1808 indicating that a fall wasoccurred. However, based on additional information received by thefusion module 1810, the fusion module 1810 can override the fall data1808, and determine that a fall was not occurred. The fusion module 1810outputs confirmation data 1812 confirming whether the user hasexperienced a fall, and if so, the type or nature of the fall.

In some cases, the fusion module 1810 can determine whether the falldata 1808 is a false positive based on the altitude data 1802 d. Forexample, the altitude data 1802 d can be input into a filter module1814. The filter module 1814 can be used to isolate particularcomponents of the altitude data 1802 d (e.g., particular frequencies orfrequency ranges). The filtered altitude data 1802 d is input into afeature extraction module 1816, which determines feature data 1818indicating one or more features of the altitude of the mobile device. Asan example, the feature extraction module 1816 can determine the changein altitude or elevation of the mobile over a period of time. Thefeature data 1818 is input into the fusion module 1810, and can be usedto identify potential false positives. For example, if the mobile deviceexperienced a significant change in elevation (e.g., several feet, orseveral levels or stories), the fusion module 1810 may determine that afalse positive is less likely. As another example, if the mobile devicedid not experience any change in elevation, the fusion module 1810 maydetermine that a false positive is more likely.

In some cases, the fusion module 1810 can determine whether the falldata 1808 is a false positive based on the heart rate data 1802 e. Forexample, the heart rate data 1802 e can be input into a filter module1820. The filter module 1820 can be used to isolate particularcomponents of the heart rate data 1802 e (e.g., particular frequenciesor frequency ranges). The filtered heart rate data 1802 e is input intoa feature extraction module 1822, which determines feature data 1824indicating one or more features of the heart rate of the user wearingthe mobile device. As another example, the feature extraction module1822 can determine an elevation or increase of the user's heart rateafter a fall. As another example, the feature extraction module 1822 candetermine a subsequent decay or recovery of the heart rate (e.g., as theheart rate returns to normal). As another example, the featureextraction module 1822 can determine a decay time constraint associatedwith the decay or recovery in heart rate (e.g., a time constantindicating the rate of decay after elevation). The feature data 1824 isinput into the fusion module 1810, and can be used to identify potentialfalse positives. For example, if the user's heart rate increased (e.g.,due to a fight-or-flight response), the fusion module 1810 may determinethat a false positive is less likely. As another example, a user's rateheart often decays more quickly after a fall than after a period ofexercise. Accordingly, the mobile device can compare the user's decaytime constant to a decay time constant sampled after the user hadexercised. If the user's decay time constant is smaller than theexercise-related decay time constant, the fusion module 1810 maydetermine that a false positive is less likely.

As described above, the mobile device can automatically transmit adistress call to a third party (e.g., an emergency responder) inresponse to detecting a fall, and determining that the user may be inneed of assistance. In a similar manner as described above (e.g., withrespect to FIG. 12), the mobile device can first alert a user regardinga potential transmission of a distress call (e.g., after a fall), andallow the user to confirm whether to proceed with the call. The user canmanually initiate the distress call in response to the alert (e.g., caninputting a command into the mobile device). However, if the user doesnot respond to the alert, the mobile device can determine whether thecall should proceed based on the user's behavior after the call. Forexample, if the user does not move after the fall (e.g., indicating thatthe user is injured or unresponsive), the mobile device can proceed withthe call. However, if the user exhibits activity (e.g., indicating thatthe user has recovered), the mobile device can cancel the call. Thisgradual escalation can be beneficial, for example, in reducing thenumber of false positives regarding a fall, and decreasing thelikelihood that a third party will be called unnecessarily.

In some cases, a mobile device can determine whether to transmit adistress call based on measurements obtained by several differentsensors. As an example, FIG. 19 shows a distress call module 1900 usedto determine whether to transmit a distress call to a third party. Thedistress call module 1900 can be implemented, for example, using themobile device 102 and/or the system 100 shown in FIG. 1.

The distress call module 1900 includes a fusion module 1902 fordetermining whether a user has fallen, and if so, the type or nature ofthe fall. The fusion module 1902 can operate in a similar manner asdescribed with respect to FIG. 18. For example, the fusion module 1902can receive several types of sensor data 1804, such as accelerationdata, gyroscopic data, location data, altitude data, and/or heart ratedata. Based on this information, the fusion module 1902 can determinewhether a user wearing the mobile device has fallen, and identifypotential false positives. The fusion module 1902 outputs confirmationdata 1804 confirming whether the user has experienced a fall, and if so,the type or nature of the fall.

Further, the distress call module 1900 determines information regardingthe motion of the user wearing the mobile device. For example, theaccelerometer data and gyroscopic data can be input into a featureextraction module 1806. The feature extraction module 1806 determinesone or more features regarding the motion of the user, such as whetherthe user has moved for a period of time after a fall, whether the usercan taken any steps after a fall, whether the user has stood up after afall, or other features. The extraction module 1806 outputs feature data1808 indicating each of the extracted features.

The confirmation data 1804 and the feature data 1806 can input into afall state machine 1810. The fall state machine 1810 determines, basedon the inputs, whether to transmit a distress call to a third party. Anexample fall state machine 1810 is shown in FIG. 20.

In this example, the mobile device begins in a “nominal” state 2002(e.g., a low alert state), a “confirmed fall” state 2004 (e.g., anelevated alert state upon detection of a fall by the fusion module1802), an “alert” state 2006 (e.g., a state in which the mobile devicealerts the user of the mobile device regarding a fall and thepossibility of sending a distress call to a third-party), a “wait” state2008 (e.g., a state in which the mobile device waits for additionalinformation, such as a potential input by a user), a “cancel” state 2010(e.g., a state in which an impending distress call is canceled), and an“SOS” state 2012 (e.g., a state in which a distress call is conducted).The mobile device can transition between each of the states based on thedetection of certain signatures of a fall (e.g., as described herein),detected periods of quiescence (e.g., a lack of movement by the user),and/or inputs by the user.

As an example, the mobile device begins at the “nominal” state 2002.Upon detection of a confirmed fall (e.g., by the fusion module 1802),the mobile device transitions of the “confirmed fall” state 2004. Upondetection of a period of quiescence T_(Q) after the fall, the mobiledevice transitions to the “alert” state 2006, and alerts the user of thedetected fall and informs the user of the possibility of sending adistress call to a third-party (e.g., an emergency responder). Themobile device transitions to the “wait” state 2008 and awaits input fromthe user in response to the alert. If no user movement is detectedduring a long-lie period of time T_(LL) after the transmission of thefall alert (e.g., 30 seconds), the mobile device transmits to the “SOS”state 2012 and transmits the distress call. The mobile device thenreturns to the “nominal” state 2002. This can be useful, for example, ifthe user has fallen and becomes incapacitated for a lengthy period oftime. The mobile device can automatically summon help for the user, evenwithout the user's input.

As another example, the mobile device begins at the “nominal” state2002. Upon detection of a confirmed fall, the mobile device transitionsof the “confirmed fall” state 2004. Upon detection of certain types ofmovements after the fall within the period of quiescence T_(Q) (e.g.,stepping movements, standing movements, high dynamic movement, or anyother movement indicative of recovery by the user), the mobile devicereturns to the “nominal” state 2002. This can be useful, for example, ifthe user has fallen, but exhibits signs of movement and recovery afterthe fall. The mobile device can refrain from alerting the user orautomatically summoning help for the user under this circumstance.

As an example, the mobile device begins at the “nominal” state 2002.Upon detection of a confirmed fall (e.g., by the fusion module 1802),the mobile device transitions of the “confirmed fall” state 2004. Upondetection of a period of quiescence T_(Q) after the fall, the mobiledevice transitions to the “alert” state 2006, and alerts the user of thedetected fall and informs the user of the possibility of sending adistress call to a third-party (e.g., an emergency responder). Themobile device transitions to the “wait” state 2008 and awaits input fromthe user in response to the alert. Upon detection of certain types ofmovements within the long-lie period of time T_(LL) after thetransmission of the fall alert (e.g., stepping movements, standingmovements, high dynamic movement, or any other movement indicative ofrecovery by the user), the mobile device transitions to the “cancel”state 2010, and does not transmit a distress call. The mobile devicethen returns to the “nominal” state 2002. This can be useful, forexample, if the user has fallen, but exhibits signs of recovery. Themobile device can alert the user regarding the fall, but does notautomatically summon help for the user under this circumstance.

Although time values are described with respect to FIG. 20, there aremerely illustrative examples. In practice, one or more of the timevalues can differ, depending on the implementation. In some cases, oneor more of the time values can be tunable parameters (e.g., parametersthat are selected empirically to distinguish between different types orevents or conditions).

Example Processes

An example process 1700 for determining whether a user has fallen and/ormay be in need of assistance using a mobile device is shown in FIG. 21.The process 2100 can be performed for example, using the mobile device102 and/or the system 100 shown in FIG. 1. In some cases, some or all ofthe process 2100 can be performed by a co-processor of the mobiledevice. The co-processor can be configured to receive motion dataobtained from one or more sensors, process the motion data, and providethe processed motion data to one or more processors of the mobiledevice.

In the process 2100, a mobile device (e.g., the mobile device 102 and/orone or more components of the system 100) obtains motion data indicatingmotion measured by a motion sensor over a time period (step 2102). Thesensor is worn by a user. As an example, as described with respect toFIGS. 1 and 2A, a user can attach a mobile device, such as a smartwatch, to his arm or wrist, and goes about his daily life. This caninclude, for example, walking, running, sitting, laying down,participating in a sport or athletic activity, or any other physicalactivity. During this time, the mobile device uses a motion sensor inthe mobile device (e.g., an accelerometer) to measure an accelerationexperienced by the sensor over a period of time. Sensor data can bepresented in the form of a time-varying acceleration signal (e.g., asshown in FIG. 3).

The mobile device determines an impact experienced by the user based onthe motion data (step 2104), the impact occurring during a firstinterval of the time period. Example techniques for determining animpact are described above (e.g., with respect to FIGS. 3-5).

The mobile device determines, based on the motion data, one or morefirst motion characteristics of the user during a second interval of thetime period (step 2106), the second interval occurring prior to thefirst interval. The second interval can be, for example, a “pre-impact”time period. Determining the first motion characteristics can include,for example, determining that the user was walking during the secondinterval, determining that the user was ascending or descending stairsduring the second interval, and/or determining that the user was movinga body part according to a flailing motion or a bracing motion duringthe second interval. Example techniques for determining motioncharacteristics during the “pre-impact” time period are described above(e.g., with respect to FIG. 6).

The mobile device determines, on the motion data, one or more secondmotion characteristics of the user during a third interval of the timeperiod (step 2108), the third interval occurring after the firstinterval. The third interval can be, for example, a “post-impact” timeperiod. Determining the second motion characteristics can include, forexample, determining that the user was walking during the thirdinterval, determining that the user was standing during the thirdinterval, and/or determining that an orientation of a body part of theuser changed N or more times during the third interval. Exampletechniques for determining motion characteristics during the“pre-impact” time period are described above (e.g., with respect toFIGS. 7 and 8).

The mobile device determines that the user has fallen based on theimpact, the one or more first motion characteristics of the user, andthe one or more second motion characteristics of the user (step 2110).The mobile device can also determine that whether the user may be inneed of assistance (e.g., as a result of the fall). Example techniquesfor determining whether a user has fallen and may be in need ofassistance are described above (e.g., with respect to FIG. 9).

As an example, the mobile device can determine, based on the motiondata, that the impact is greater than a first threshold value, anddetermining, based on the motion data, that a motion of the user wasimpaired during the third interval. Based on these determinations, themobile device can determine that the user has fallen and may be in needof assistance.

As another example, the mobile device can determine, based on the motiondata, that the impact is less than a first threshold value and greaterthan a second threshold value. Further, the mobile device can determine,based on the motion data, that the user was at least one of walkingduring the second interval, ascending stairs during the second interval,or descending stairs during the second interval. Further, the mobiledevice can determine, based on the motion data, that the user was movinga body part according to a flailing motion or a bracing motion duringthe second interval. Further, the mobile device can determine, based onthe motion data, that a motion of the user was impaired during the thirdinterval. Based on these determinations, the mobile device can determinethat the user has fallen and may be in need of assistance.

In some cases, the mobile device can determine that the user has fallenbased on a statistical model (e.g., a Bayesian statistical model). Forexample, a statistical model can be generated based on one or moresampled impacts, one or more sampled first motion characteristics, andone or more sampled second motion characteristics. The one or moresampled impacts, the one or more sampled first motion characteristics,and the one or more sampled second motion characteristics can bedetermined based on sample motion data collected from a samplepopulation. The sample motion data can indicate motion measured by oneor more additional motion sensors over one or more additional timeperiods, where each additional motion sensor is worn by a respectiveuser of the sample population. Example techniques for generating andusing statistical model are described above. In some implementations,the one or more sampled first motion characteristics can include anindication of a type of activity being performed by a particularadditional user with respect to the sample motion data, an indication ofan activity level of a particular additional user with respect to thesample motion data, and/or an indication of a walking speed of aparticular additional user with respect to the sample motion data.

Responsive to determining that the user has fallen, the mobile devicegenerates a notification indicating that the user has fallen (step2112). As an example, the mobile device can present an indication thatthe user has fallen on a display device and/or an audio device of themobile device. As another example, the mobile device can transmit datato a communications device remote from the mobile device indicating thatthe user has fallen. This can include, for example, an e-mail, instantchat message, text message, telephone message, fax message, radiomessage, audio message, video message, haptic message, or anothermessage for conveying information. Example techniques for generatingnotifications are described above.

Another example process 2200 for determining whether a user has fallenand/or may be in need of assistance using a mobile device is shown inFIG. 22. The process 2200 can be performed for example, using the mobiledevice 102 and/or the system 100 shown in FIG. 1. In some cases, some orall of the process 2200 can be performed by a co-processor of the mobiledevice. The co-processor can be configured to receive motion dataobtained from one or more sensors, process the motion data, and providethe processed motion data to one or more processors of the mobiledevice.

In the process 2200, a mobile device (e.g., the mobile device 102 and/orone or more components of the system 100) obtains a first signalindicating an acceleration measured by an accelerometer over a timeperiod (step 2202), and a second signal indicating an orientationmeasured by an orientation sensor over the time period (step 2204). Theaccelerometer and the orientation sensor are physically coupled to auser. As an example, as described with respect to FIGS. 1 and 2A, a usercan attach a mobile device, such as a smart watch, to his arm or wrist,and goes about his daily life. This can include, for example, walking,running, sitting, laying down, participating in a sport or athleticactivity, or any other physical activity. During this time, the mobiledevice uses sensors in the mobile device (e.g., an accelerometer andorientation sensor, such as a gyroscope) to measure an accelerationexperienced by the sensors over a period of time and an orientation ofthe sensors over the period of time. Sensor data can be presented in theform of a time-varying signal (e.g., as shown in FIG. 11A).

The mobile device determines rotation data regarding an amount ofrotation experienced by the user during the time period (step 2206). Therotational data can include a third signal corresponding to a rotationrate of the rotation by the user during the time period, an indicationof one or more rotational axes of the rotation in a reference coordinatesystem by the user during the time period (e.g., one or moreinstantaneous axes of rotation), and/or an indication of an averagerotational axis of the rotation by the user during the time period.Examples rotational data is shown and described, for instance, in FIGS.11A-11D.

The mobile device determines that the user has tumbled based on therotation data (step 2208). In some cases, this can be performed bydetermining a variation between the one or more rotational axes of therotation by the user during the time period and the average rotationalaxis of the rotation by the user during the time period. Further, themobile device can determine that the variation is less than a firstthreshold value. Responsive to determining that the variation is lessthan the first threshold value, the mobile device can determine a fourthsignal corresponding to an angular displacement by the user during thetime period based on the third signal (e.g., by integrating the thirdsignal with respect to the period of time).

Further, the mobile device can determine that the angular displacementby the user during the period of time is greater than a second thresholdvalue, and determine that at least one of the one or more rotationalaxes of the rotation by the user during the period of time is greaterthan a third threshold value. Responsive to determining that the angulardisplacement by the user during the period of time is greater than thesecond threshold value and determining that at least one of the one ormore rotational axes of the rotation by the user during the period oftime is greater than the third threshold value, the mobile device candetermine that the user has tumbled. Otherwise, the mobile device candetermine that the user has not tumbled.

Responsive to determining that the user has tumbled, the mobile devicegenerates a notification indicating that the user has tumbled (step2210). Generating the notification can include presenting an indicationthat the user has tumbled on at least one of a display device or anaudio device of the mobile device and/or transmitting data to acommunications device remote from the mobile device. This can include,for example, an e-mail, instant chat message, text message, telephonemessage, fax message, radio message, audio message, video message,haptic message, or another message for conveying information. The datacan include an indication that the user has tumbled. Example techniquesfor generating notifications are described above.

Another example process 2300 for determining whether a user has fallenand/or may be in need of assistance using a mobile device is shown inFIG. 23. The process 2200 can be performed for example, using the mobiledevice 102 and/or the system 100 shown in FIG. 1. In some cases, some orall of the process 2300 can be performed by a co-processor of the mobiledevice. The co-processor can be configured to receive motion dataobtained from one or more sensors, process the motion data, and providethe processed motion data to one or more processors of the mobiledevice.

In the process 2300, a mobile device (e.g., the mobile device 102 and/orone or more components of the system 100) obtains motion data indicatinga motion measured by one or more motion sensors over a first time period(step 2302). The one or more motion sensors are worn by a user. The oneor more motion sensors can include an accelerometer and/or a gyroscope.The mobile device can be a wearable mobile device. Example techniquesfor obtaining motion data are described above.

The mobile device determines that the user has fallen based on themotion data (step 2304). In some implementations, the mobile device candetermine that the user has fallen by determining that the userexperienced an impact based on the motion data.). In someimplementations, the mobile device can determine that the user hasfallen by determining a behavior of the user during the first timeperiod. Example techniques for determining whether a user has fallen aredescribed above.

Responsive to determining that the user has fallen, the mobile devicegenerates one or more notifications indicating that the user has fallen(step 2306). In some implementations, generating the one or morenotifications can include presenting a first notification to the userindicating that the user has fallen. The first notification can includeat least one of a visual message, an audio message, or a haptic message.Example techniques for generating notifications are described above.

In some implementations, the mobile device can receive an input from theuser in response to the first notification (e.g., an input indicating arequest for assistance by the user). In response to receiving the input,the mobile device can transmit a second notification indicating therequest for assistance to a communications device remote from the mobiledevice. The communications device can be an emergency response system.Further, the second notification can indicate a location of the mobiledevice.

In some implementations, the mobile device can determining an absence ofmovement by the user during a second time period after the user hasfallen (e.g., indicating that the user is injured or incapacitated).Responsive to determining the absence of movement by the user during thesecond time period, the mobile device can transmit a second notificationindicating a request for assistance to a communications device remotefrom the mobile device.

In some implementations, the mobile device can determine that the userhas moved during a second time period after the user has fallen (e.g.,walked, stood up, or perhaps some other type of movement). Responsive todetermining that the user has moved during the second time period, themobile device can refrain from transmitting a second notificationindicating a request for assistance to a communications device remotefrom the mobile device.

In some implementations, the one or more notifications can be generatedaccording to a state machine. Example state machines are shown in FIGS.12 and 20.

Another example process 2400 for determining whether a user has fallenand/or may be in need of assistance using a mobile device is shown inFIG. 24. The process 2200 can be performed for example, using the mobiledevice 102 and/or the system 100 shown in FIG. 1. In some cases, some orall of the process 2400 can be performed by a co-processor of the mobiledevice. The co-processor can be configured to receive motion dataobtained from one or more sensors, process the motion data, and providethe processed motion data to one or more processors of the mobiledevice.

In the process 2400, a mobile device (e.g., the mobile device 102 and/orone or more components of the system 100) obtains sample data generatedby a plurality of sensors over a time period (step 2402). The pluralityof sensors is worn by a user. The sample data includes motion dataindicating a motion of the user obtained from one or more motion sensorsof the plurality of sensors. The sample data also includes at least oneof location data indicating a location of the mobile device obtainedfrom one or more location sensors of the plurality of sensors, altitudedata indicating an altitude of the mobile device obtained from one ormore altitude sensors of the plurality of sensors, or heart rate dataindicating a heart rate of the user obtained from one or more heart ratesensor of the plurality of sensors. The mobile device can be a wearablemobile device. Example techniques for obtaining sample data aredescribed above.

In some implementations, the one or more motion sensors can include anaccelerometer and/or a gyroscope. In some implementations, anaccelerometer and a gyroscope can be independently operated to acquiremotion data. For example, acceleration data can be obtained using theaccelerometer during a first time interval during the period of time.The gyroscope can be disabled during the first time interval. Further,based on the accelerometer data obtained during the first time interval,the mobile device can determine that a movement of a user exceeded athreshold level during the first time interval. Responsive todetermining that the movement of the user exceeded the threshold levelduring the first time interval, the mobile device can obtainacceleration data using the accelerometer and gyroscope data using thegyroscope during a second time interval after the first time interval.In some cases, the accelerometer and the gyroscope can be operatedaccording to a state machine. An example state machine is shown in FIG.14.

In some implementations, the one or more altitude sensors can include atleast one of an altimeter or a barometer. The altitude sensors can beused, for example, to measure certain changes in altitude indicate of afall (e.g., a decrease in altitude indicative of falling off a ladder orstructure).

In some implementations, the one or more location sensors can include atleast one of a wireless transceiver (e.g., a Wi-Fi radio or cellularradio) or a global Navigation Satellite System receiver (e.g., a GPSreceiver).

The mobile devices determines that the user has fallen based on thesample data (step 2404). Example techniques for determining whether auser has fallen are described above.

In some implementations, the mobile device can determine that the userhas fallen by determining, based on the motion data, a change inorientation of the mobile device (e.g., a pose angle) during the periodof time, and determining that the user has fallen based on the change inorientation.

In some implementations, the mobile device can determine that the userhas fallen by determining, based on the motion data, an impactexperienced by the user during the period of time, and determining thatthe user has fallen based on the impact.

In some implementations, the mobile device can determine that the userhas fallen by determining, based on the altitude data, a change inaltitude of the mobile device during the period of time, and determiningthat the user has fallen based on the change in altitude.

In some implementations, the mobile device can determine that the userhas fallen by determining, based on the heart rate data, a change inheart rate of the user during the period of time, and determining thatthe user has fallen based on the change in heart rate. Determining thechange in heart rate of the user during the period of time cab includedetermining a rate of decay of the heart rate of the user during theperiod of time (e.g., a time constant associated with the rate ofdecay).

In some implementations, the mobile device can determine that the userhas fallen by determining, based on the location data, an environmentalcondition at the location of the mobile device, and determining that theuser has fallen based on the environment conditional. The environmentalcondition can be a weather at the location (e.g., rain, snow, etc.).

In some implementations, the mobile device can determine that the userhas fallen based on the motion data, the location data, the altitudedata, and the heart rate data (e.g., in conjunction).

Responsive to determining that the user has fallen, the mobile devicegenerates one or more notifications indicating that the user has fallen(step 2406). Generating the one or more notifications can includetransmitting a notification to a communications device remote from themobile device. The communications device can be emergency responsesystem. Example techniques for generating notifications are describedabove.

Example Mobile Device

FIG. 25 is a block diagram of an example device architecture 2500 forimplementing the features and processes described in reference to FIGS.1-24. For example, the architecture 2500 can be used to implement themobile device 102, the server computer system 104, and/or one or more ofthe communications devices 106. Architecture 2500 may be implemented inany device for generating the features described in reference to FIGS.1-24, including but not limited to desktop computers, server computers,portable computers, smart phones, tablet computers, game consoles,wearable computers, set top boxes, media players, smart TVs, and thelike.

The architecture 2500 can include a memory interface 2502, one or moredata processor 2504, one or more data co-processors 2574, and aperipherals interface 2506. The memory interface 2502, the processor(s)2504, the co-processor(s) 2574, and/or the peripherals interface 2506can be separate components or can be integrated in one or moreintegrated circuits. One or more communication buses or signal lines maycouple the various components.

The processor(s) 2504 and/or the co-processor(s) 2574 can operate inconjunction to perform the operations described herein. For instance,the processor(s) 2504 can include one or more central processing units(CPUs) that are configured to function as the primary computerprocessors for the architecture 2500. As an example, the processor(s)2504 can be configured to perform generalized data processing tasks ofthe architecture 2500. Further, at least some of the data processingtasks can be offloaded to the co-processor(s) 2574. For example,specialized data processing tasks, such as processing motion data,processing image data, encrypting data, and/or performing certain typesof arithmetic operations, can be offloaded to one or more specializedco-processor(s) 2574 for handling those tasks. In some cases, theprocessor(s) 2504 can be relatively more powerful than theco-processor(s) 2574 and/or can consume more power than theco-processor(s) 2574. This can be useful, for example, as it enables theprocessor(s) 2504 to handle generalized tasks quickly, while alsooffloading certain other tasks to co-processor(s) 2574 that may performthose tasks more efficiency and/or more effectively. In some cases, aco-processor(s) can include one or more sensors or other components(e.g., as described herein), and can be configured to process dataobtained using those sensors or components, and provide the processeddata to the processor(s) 2504 for further analysis.

Sensors, devices, and subsystems can be coupled to peripherals interface2506 to facilitate multiple functionalities. For example, a motionsensor 2510, a light sensor 2512, and a proximity sensor 2514 can becoupled to the peripherals interface 2506 to facilitate orientation,lighting, and proximity functions of the architecture 2500. For example,in some implementations, a light sensor 2512 can be utilized tofacilitate adjusting the brightness of a touch surface 2546. In someimplementations, a motion sensor 2510 can be utilized to detect movementand orientation of the device. For example, the motion sensor 2510 caninclude one or more accelerometers (e.g., to measure the accelerationexperienced by the motion sensor 2510 and/or the architecture 2500 overa period of time), and/or one or more compasses or gyros (e.g., tomeasure the orientation of the motion sensor 2510 and/or the mobiledevice). In some cases, the measurement information obtained by themotion sensor 2510 can be in the form of one or more a time-varyingsignals (e.g., a time-varying plot of an acceleration and/or anorientation over a period of time). Further, display objects or mediamay be presented according to a detected orientation (e.g., according toa “portrait” orientation or a “landscape” orientation). In some cases, amotion sensor 2510 can be directly integrated into a co-processor 2574configured to processes measurements obtained by the motion sensor 2510.For example, a co-processor 2574 can include one more accelerometers,compasses, and/or gyroscopes, and can be configured to obtain sensordata from each of these sensors, process the sensor data, and transmitthe processed data to the processor(s) 2504 for further analysis.

Other sensors may also be connected to the peripherals interface 2506,such as a temperature sensor, a biometric sensor, or other sensingdevice, to facilitate related functionalities. As an example, as shownin FIG. 25, the architecture 2500 can include a heart rate sensor 2532that measures the beats of a user's heart. Similarly, these othersensors also can be directly integrated into one or more co-processor(s)2574 configured to process measurements obtained from those sensors.

A location processor 2515 (e.g., a GNSS receiver chip) can be connectedto the peripherals interface 2506 to provide geo-referencing. Anelectronic magnetometer 2516 (e.g., an integrated circuit chip) can alsobe connected to the peripherals interface 2506 to provide data that maybe used to determine the direction of magnetic North. Thus, theelectronic magnetometer 2516 can be used as an electronic compass.

A camera subsystem 2520 and an optical sensor 2522 (e.g., a chargedcoupled device [CCD] or a complementary metal-oxide semiconductor [CMOS]optical sensor) can be utilized to facilitate camera functions, such asrecording photographs and video clips.

Communication functions may be facilitated through one or morecommunication subsystems 2524. The communication subsystem(s) 2524 caninclude one or more wireless and/or wired communication subsystems. Forexample, wireless communication subsystems can include radio frequencyreceivers and transmitters and/or optical (e.g., infrared) receivers andtransmitters. As another example, wired communication system can includea port device, e.g., a Universal Serial Bus (USB) port or some otherwired port connection that can be used to establish a wired connectionto other computing devices, such as other communication devices, networkaccess devices, a personal computer, a printer, a display screen, orother processing devices capable of receiving or transmitting data.

The specific design and implementation of the communication sub system2524 can depend on the communication network(s) or medium(s) over whichthe architecture 2500 is intended to operate. For example, thearchitecture 2500 can include wireless communication subsystems designedto operate over a global system for mobile communications (GSM) network,a GPRS network, an enhanced data GSM environment (EDGE) network, 802.xcommunication networks (e.g., Wi-Fi, Wi-Max), code division multipleaccess (CDMA) networks, NFC and a Bluetooth™ network. The wirelesscommunication subsystems can also include hosting protocols such thatthe architecture 2500 can be configured as a base station for otherwireless devices. As another example, the communication subsystems mayallow the architecture 2500 to synchronize with a host device using oneor more protocols, such as, for example, the TCP/IP protocol, HTTPprotocol, UDP protocol, and any other known protocol.

An audio subsystem 2526 can be coupled to a speaker 2528 and one or moremicrophones 2530 to facilitate voice-enabled functions, such as voicerecognition, voice replication, digital recording, and telephonyfunctions.

An I/O subsystem 2540 can include a touch controller 2542 and/or otherinput controller(s) 2544. The touch controller 2542 can be coupled to atouch surface 2546. The touch surface 2546 and the touch controller 2542can, for example, detect contact and movement or break thereof using anyof a number of touch sensitivity technologies, including but not limitedto capacitive, resistive, infrared, and surface acoustic wavetechnologies, as well as other proximity sensor arrays or other elementsfor determining one or more points of contact with the touch surface2546. In one implementation, the touch surface 2546 can display virtualor soft buttons and a virtual keyboard, which can be used as aninput/output device by the user.

Other input controller(s) 2544 can be coupled to other input/controldevices 2548, such as one or more buttons, rocker switches, thumb-wheel,infrared port, USB port, and/or a pointer device such as a stylus. Theone or more buttons (not shown) can include an up/down button for volumecontrol of the speaker 2528 and/or the microphone 2530.

In some implementations, the architecture 2500 can present recordedaudio and/or video files, such as MP3, AAC, and MPEG video files. Insome implementations, the architecture 2500 can include thefunctionality of an MP3 player and may include a pin connector fortethering to other devices. Other input/output and control devices maybe used.

A memory interface 2502 can be coupled to a memory 2550. The memory 2550can include high-speed random access memory or non-volatile memory, suchas one or more magnetic disk storage devices, one or more opticalstorage devices, or flash memory (e.g., NAND, NOR). The memory 2550 canstore an operating system 2552, such as Darwin, RTXC, LINUX, UNIX, OS X,WINDOWS, or an embedded operating system such as VxWorks. The operatingsystem 2552 can include instructions for handling basic system servicesand for performing hardware dependent tasks. In some implementations,the operating system 2552 can include a kernel (e.g., UNIX kernel).

The memory 2550 can also store communication instructions 2554 tofacilitate communicating with one or more additional devices, one ormore computers or servers, including peer-to-peer communications. Thecommunication instructions 2554 can also be used to select anoperational mode or communication medium for use by the device, based ona geographic location (obtained by the GPS/Navigation instructions 2568)of the device. The memory 2550 can include graphical user interfaceinstructions 2556 to facilitate graphic user interface processing,including a touch model for interpreting touch inputs and gestures;sensor processing instructions 2558 to facilitate sensor-relatedprocessing and functions; phone instructions 2560 to facilitatephone-related processes and functions; electronic messaging instructions2562 to facilitate electronic-messaging related processes and functions;web browsing instructions 2564 to facilitate web browsing-relatedprocesses and functions; media processing instructions 2566 tofacilitate media processing-related processes and functions;GPS/Navigation instructions 2569 to facilitate GPS andnavigation-related processes; camera instructions 2570 to facilitatecamera-related processes and functions; and other instructions 2572 forperforming some or all of the processes described herein.

Each of the above identified instructions and applications cancorrespond to a set of instructions for performing one or more functionsdescribed herein. These instructions need not be implemented as separatesoftware programs, procedures, or modules. The memory 2550 can includeadditional instructions or fewer instructions. Furthermore, variousfunctions of the device may be implemented in hardware and/or insoftware, including in one or more signal processing and/or applicationspecific integrated circuits (ASICs).

The features described may be implemented in digital electroniccircuitry or in computer hardware, firmware, software, or incombinations of them. The features may be implemented in a computerprogram product tangibly embodied in an information carrier, e.g., in amachine-readable storage device, for execution by a programmableprocessor; and method steps may be performed by a programmable processorexecuting a program of instructions to perform functions of thedescribed implementations by operating on input data and generatingoutput.

The described features may be implemented advantageously in one or morecomputer programs that are executable on a programmable system includingat least one programmable processor coupled to receive data andinstructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that may be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program may be written in anyform of programming language (e.g., Objective-C, Java), includingcompiled or interpreted languages, and it may be deployed in any form,including as a stand-alone program or as a module, component,subroutine, or other unit suitable for use in a computing environment.

Suitable processors for the execution of a program of instructionsinclude, by way of example, both general and special purposemicroprocessors, and the sole processor or one of multiple processors orcores, of any kind of computer. Generally, a processor will receiveinstructions and data from a read-only memory or a random access memoryor both. The essential elements of a computer are a processor forexecuting instructions and one or more memories for storing instructionsand data. Generally, a computer may communicate with mass storagedevices for storing data files. These mass storage devices may includemagnetic disks, such as internal hard disks and removable disks;magneto-optical disks; and optical disks. Storage devices suitable fortangibly embodying computer program instructions and data include allforms of non-volatile memory, including by way of example semiconductormemory devices, such as EPROM, EEPROM, and flash memory devices;magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor andthe memory may be supplemented by, or incorporated in, ASICs(application-specific integrated circuits).

To provide for interaction with a user the features may be implementedon a computer having a display device such as a CRT (cathode ray tube)or LCD (liquid crystal display) monitor for displaying information tothe author and a keyboard and a pointing device such as a mouse or atrackball by which the author may provide input to the computer.

The features may be implemented in a computer system that includes aback-end component, such as a data server or that includes a middlewarecomponent, such as an application server or an Internet server, or thatincludes a front-end component, such as a client computer having agraphical user interface or an Internet browser, or any combination ofthem. The components of the system may be connected by any form ormedium of digital data communication such as a communication network.Examples of communication networks include a LAN, a WAN and thecomputers and networks forming the Internet.

The computer system may include clients and servers. A client and serverare generally remote from each other and typically interact through anetwork. The relationship of client and server arises by virtue ofcomputer programs running on the respective computers and having aclient-server relationship to each other.

One or more features or steps of the disclosed embodiments may beimplemented using an Application Programming Interface (API). An API maydefine on or more parameters that are passed between a callingapplication and other software code (e.g., an operating system, libraryroutine, function) that provides a service, that provides data, or thatperforms an operation or a computation.

The API may be implemented as one or more calls in program code thatsend or receive one or more parameters through a parameter list or otherstructure based on a call convention defined in an API specificationdocument. A parameter may be a constant, a key, a data structure, anobject, an object class, a variable, a data type, a pointer, an array, alist, or another call. API calls and parameters may be implemented inany programming language. The programming language may define thevocabulary and calling convention that a programmer will employ toaccess functions supporting the API.

In some implementations, an API call may report to an application thecapabilities of a device running the application, such as inputcapability, output capability, processing capability, power capability,communications capability, etc.

As described above, some aspects of the subject matter of thisspecification include gathering and use of data available from varioussources to improve services a mobile device can provide to a user. Thepresent disclosure contemplates that in some instances, this gathereddata may identify a particular location or an address based on deviceusage. Such personal information data can include location-based data,addresses, subscriber account identifiers, or other identifyinginformation.

The present disclosure further contemplates that the entitiesresponsible for the collection, analysis, disclosure, transfer, storage,or other use of such personal information data will comply withwell-established privacy policies and/or privacy practices. Inparticular, such entities should implement and consistently use privacypolicies and practices that are generally recognized as meeting orexceeding industry or governmental requirements for maintaining personalinformation data private and secure. For example, personal informationfrom users should be collected for legitimate and reasonable uses of theentity and not shared or sold outside of those legitimate uses. Further,such collection should occur only after receiving the informed consentof the users. Additionally, such entities would take any needed stepsfor safeguarding and securing access to such personal information dataand ensuring that others with access to the personal information dataadhere to their privacy policies and procedures. Further, such entitiescan subject themselves to evaluation by third parties to certify theiradherence to widely accepted privacy policies and practices.

In the case of advertisement delivery services, the present disclosurealso contemplates embodiments in which users selectively block the useof, or access to, personal information data. That is, the presentdisclosure contemplates that hardware and/or software elements can beprovided to prevent or block access to such personal information data.For example, in the case of advertisement delivery services, the presenttechnology can be configured to allow users to select to “opt in” or“opt out” of participation in the collection of personal informationdata during registration for services.

Therefore, although the present disclosure broadly covers use ofpersonal information data to implement one or more various disclosedembodiments, the present disclosure also contemplates that the variousembodiments can also be implemented without the need for accessing suchpersonal information data. That is, the various embodiments of thepresent technology are not rendered inoperable due to the lack of all ora portion of such personal information data. For example, content can beselected and delivered to users by inferring preferences based onnon-personal information data or a bare minimum amount of personalinformation, such as the content being requested by the deviceassociated with a user, other non-personal information available to thecontent delivery services, or publicly available information.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made. Elements of one ormore implementations may be combined, deleted, modified, or supplementedto form further implementations. As yet another example, the logic flowsdepicted in the figures do not require the particular order shown, orsequential order, to achieve desirable results. In addition, other stepsmay be provided, or steps may be eliminated, from the described flows,and other components may be added to, or removed from, the describedsystems. Accordingly, other implementations are within the scope of thefollowing claims.

What is claimed is:
 1. A method comprising: receiving, by a computingdevice, motion data obtained by one or more sensors over a time period,wherein the one or more sensors are worn by a user; determining, by thecomputing device, an impact experienced by the user based on the motiondata, the impact occurring during a time interval of the time period;determining, by the computing device based on the motion data, motioncharacteristics of the user prior to the time interval, during the timeinterval, and after the time interval determining, by the computingdevice, that the user has fallen based on the impact and the motioncharacteristics of the user; and responsive to determining that the userhas fallen, generating, by the computing device, a notificationindicating that the user has fallen.
 2. The method of claim 1, whereindetermining the motion characteristics of the user comprisesdetermining, based on the motion data, that the user was walking priorto the time interval.
 3. The method of claim 1, wherein determining themotion characteristics comprises determining, based on the motion data,that the user was traversing stairs prior to the time interval.
 4. Themethod of claim 1, wherein determining the motion characteristicscomprises determining, based on the motion data, that the user wasmoving a body part according to a flailing motion or a bracing motionprior to the time interval.
 5. The method of claim 1, whereindetermining the motion characteristics comprises determining, based onthe motion data, that the user was walking after the time interval. 6.The method of claim 1, wherein determining the motion characteristicscomprises determining, based on the motion data, that the user wasstanding after the time interval.
 7. The method of claim 1, whereindetermining the motion characteristics comprises determining, based onthe motion data, that an orientation of a body part of the user changedone or more times after the time interval.
 8. The method of claim 1,wherein generating the notification comprises presenting an indicationthat the user has fallen on at least one of a display device or an audiodevice of the computing device.
 9. The method of claim 1, whereingenerating the notification comprises transmitting data to acommunications device remote from the computing device, the datacomprising an indication that the user has fallen.
 10. The method ofclaim 9, wherein the communications device is associated with anemergency response system.
 11. The method of claim 1, whereindetermining that the user has fallen comprises: generating a statisticalmodel based on one or more sampled impacts and one or more sampledmotion characteristics, wherein the one or more sampled impacts and theone or more sampled motion characteristics are determined based onsample motion data, wherein the sample motion data indicates a motionmeasured by one or more additional sensors over one or more additionaltime periods, wherein the one or more additional sensors are worn by oneor more additional users.
 12. The method of claim 11, wherein thestatistical model is a Bayesian statistical model.
 13. The method ofclaim 11, wherein the one or more sampled motion characteristicscomprise an indication of a type of activity being performed by aparticular additional user.
 14. The method of claim 11, wherein the oneor more sampled motion characteristics comprises an indication of anactivity level of a particular additional user.
 15. The method of claim11, wherein the one or more sampled motion characteristics comprises anindication of a walking speed of a particular additional user.
 16. Themethod of claim 1, wherein the method is performed by a co-processor ofthe computing device, and wherein the co-processor is configured toreceive the motion data from the one or more sensors, process the motiondata, and provide the processed motion data to one or more processors ofthe computing device.
 17. The method of claim 1, wherein the computingdevice comprises at least some of the one or more sensors.
 18. Themethod of claim 1, wherein the mobile device is worn on an arm or awrist of the user while the motion data is obtained by the one or moresensors.
 19. The method of claim 1, wherein the computing device is awearable mobile device.
 20. The method of claim 19, wherein thecomputing device is a smart watch.
 21. A system comprising: one or moreprocessors; one or more sensors; and one or more non-transitory computerreadable media storing instructions that, when executed by the one ormore processors, cause the one or more processors to perform operationscomprising: receiving motion data obtained by the one or more sensorsover a time period, wherein the one or more sensors are worn by a user;determining an impact experienced by the user based on the motion data,the impact occurring during a time interval of the time period;determining, based on the motion data, motion characteristics of theuser prior to the time interval, during the time interval, and after thetime interval determining that the user has fallen based on the impactand the motion characteristics of the user; and responsive todetermining that the user has fallen, generating a notificationindicating that the user has fallen.
 22. The system of claim 21, whereindetermining the motion characteristics of the user comprisesdetermining, based on the motion data, that the user was walking priorto the time interval.
 23. The system of claim 21, wherein determiningthe motion characteristics comprises determining, based on the motiondata, that the user was traversing stairs prior to the time interval.24. The system of claim 21, wherein determining the motioncharacteristics comprises determining, based on the motion data, thatthe user was moving a body part according to a flailing motion or abracing motion prior to the time interval.
 25. The system of claim 21,wherein determining the motion characteristics comprises determining,based on the motion data, that the user was walking after the timeinterval.
 26. The system of claim 21, wherein determining the motioncharacteristics comprises determining, based on the motion data, thatthe user was standing after the time interval.
 27. The system of claim21, wherein determining the motion characteristics comprisesdetermining, based on the motion data, that an orientation of a bodypart of the user changed one or more times after the time interval. 28.The system of claim 21, further comprising at least one of a displaydevice or an audio device, and wherein generating the notificationcomprises presenting an indication that the user has fallen on at leastone of the display device or the audio device.
 29. The system of claim21, wherein generating the notification comprises transmitting data to acommunications device remote from the system, the data comprising anindication that the user has fallen.
 30. The system of claim 29, whereinthe communications device is associated with an emergency responsesystem.
 31. The system of claim 21, wherein determining that the userhas fallen comprises: generating a statistical model based on one ormore sampled impacts and one or more sampled motion characteristics,wherein the one or more sampled impacts and the one or more sampledmotion characteristics are determined based on sample motion data,wherein the sample motion data indicates a motion measured by one ormore additional sensors over one or more additional time periods,wherein the one or more additional sensors are worn by one or moreadditional users.
 32. The system of claim 31, wherein the statisticalmodel is a Bayesian statistical model.
 33. The system of claim 31,wherein the one or more sampled motion characteristics comprise anindication of a type of activity being performed by a particularadditional user.
 34. The system of claim 31, wherein the one or moresampled motion characteristics comprises an indication of an activitylevel of a particular additional user.
 35. The system of claim 31,wherein the one or more sampled motion characteristics comprises anindication of a walking speed of a particular additional user.
 36. Thesystem of claim 21, wherein the one or more processors comprise aco-processor, wherein the operations are performed, at least in part, bythe co-processor.
 37. The system of claim 21, wherein the system isconfigured to be worn on an arm or a wrist of the user while the motiondata is obtained by the one or more sensors.
 38. The system of claim 21,wherein the system is a wearable mobile device.
 39. The system of claim38, wherein the system is a smart watch.